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Master's Theses
12-2014
The Application of P-Bar Theory in
Transformation-Based Error-Driven Learning
Bryant Harold Walley
University of Southern Mississippi
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The University of Southern Mississippi
THE APPLICATION OF P-BAR THEORY IN
TRANSFORMATION-BASED ERROR-DRIVEN LEARNING
by
Bryant Harold Walley
A Thesis
Submitted to the Graduate School
of The University of Southern Mississippi
in Partial Fulfillment of the Requirements
for the Degree of Master of Science
Approved:
Dr. Louise Perkins
Committee Chair
Dr. Sumanth Yenduri
Dr. Joe Zhang
Dr. Karen Coats
Dean of the Graduate School
December 2014
ABSTRACT
THE APPLICATION OF P-BAR THEORY IN
TRANSFORMATION-BASED ERROR-DRIVEN LEARNING
by Bryant Harold Walley
December 2014
In P-bar Theory, Perkins et al. (2014) proposed a rule based method for
determining the context of a partext (i.e., a part of a text document).
In Transformation-Based Error-Driven Learning and Natural Language
Processing: A Case Study in Part-of-Speech Tagging Brill (1995) demonstrates a method
of error-driven learning applied to individual words at the sentence level to determine the
part of speech each word represents.
We combine these two concepts providing a transformation-based error-driven
learning algorithm to improve the results obtained from the static rules Perkins proposed
and determine if the rule order prediction will provide additional metadata.
ii
DEDICATION
The path to my master’s degree has been a very long and winding road filled with
challenges. On my journey, there were many people who contributed to me getting here.
I would like to thank each one of you:
Vivian Anderson – Northwestern Middle School
Cynthia Thomas (Thompson) – Zachary Senior High School
Patricia Waldrup – Jones County Junior College
Tim Waldrup – Jones County Junior College
Earl Benson – Jones County Junior College
And very special thanks to my mom, Lois Pulliam, for her never ending
encouragement on this long journey.
iii
ACKNOWLEDGMENTS
I would like to thank Dr. A. Louise Perkins, my committee director, for her time,
patience, and direction during this process and all of the other times over the past few
years when she encouraged me not to just “know something is” but to “know why
something is.”
I would also like to thank Dr. Sumanth Yenduri for the time he has spent over the
past few years doing whatever it took to get the information I needed to know into my
head.
I would like to give a special acknowledgement and thanks to Dr. Joe Zhang for
agreeing to serve on my thesis committee with such short notice. I am very grateful.
I would like to thank the graduate students at USM Gulf Coast for the hundreds of
hours of data collection over the past year. This thesis could not have been done without
you.
Final thanks go to Tom Rishel and Pete Sakalaukus. Your ability to teach and
apply basic and advanced fundamentals to real world situations is what gave me the
foundation to complete this thesis.
iv
TABLE OF CONTENTS
ABSTRACT ........................................................................................................................ ii
DEDICATION ................................................................................................................... iii
ACKNOWLEDGMENTS ................................................................................................. iv
LIST OF TABLES ............................................................................................................. vi
LIST OF ILLUSTRATIONS ............................................................................................ vii
CHAPTER
I.
INTRODUCTION ...................................................................................... 1
II.
AN OVERVIEW OF P-BAR THEORY .................................................... 2
III.
AN OVERVIEW OF TRANSFORMATION-BASED
ERROR-DRIVEN LEARNING ................................................................. 3
IV.
METHODOLOGY ..................................................................................... 4
V.
LOGIC ........................................................................................................ 7
VI.
DATA ANALYSIS ..................................................................................... 9
VII.
CONCLUSION ......................................................................................... 14
APPENDIXES .................................................................................................................. 15
REFERENCES ................................................................................................................163
v
LIST OF TABLES
Table
1.
Sherlock Holmes – Short Story Averages – P-bar Data Points .............................. 9
2.
Sherlock Holmes – Short Story Averages – P-bar Confidence Levels ................. 10
3.
Red Headed League – Rule Summary Data ......................................................... 11
4.
The Storm – Individual Comparison – Perception ............................................... 13
vi
LIST OF ILLUSTRATIONS
Figure
1.
Transformation-Based Error-Driven Learning ....................................................... 4
2.
Addition of Context Dictionary for initial state ...................................................... 5
3.
P-bar Translation-Based Error-Driven Learning .................................................... 6
vii
1
CHAPTER I
INTRODUCTION
In this thesis, we utilize transformation-based error-driven learning to train P-bar
theory under different circumstances to improve its accuracy. We begin with an
overview of P-bar theory and transformation-based error driven learning. We then
demonstrate how the two processes can be combined to produce a supervised learner.
We then show our results and compare our accuracy rate to data that has been handtagged and evaluated.
2
CHAPTER II
AN OVERVIEW OF P-BAR THEORY
In Perkins et al. (2014), they introduce contextual granularity at the partext level.
Norm Chomsky’s original hierarchy for natural languages works with semantic context at
the word level. In contrast, data mining traditionally identifies semantic context at the
document level. In Attention, Intentions, and the Structure of Discourse, Sidner (1986)
shows us that natural language, however, typically varies the semantic context throughout
the test.
Perkins et al. (2014) defined a vocabulary context, CV, to be a two-tuple (VS, NS)
over a vocabulary dictionary, where each word in a given context is assumed to have an
unambiguous semantic meaning that is itself an element of a semantic context CS. For a
given partext, or part of text, such as a paragraph, with this notation, they defined
contextual approximation as mapping a candidate set of vocabulary contexts (identified
based on the vocabulary words within a given partext), to a unique semantic context. The
mapping of the context dictionaries to the text is built as an analogue to the method
presented in Remarks on Nominalization. Readings in English Transformational
Grammar where Chomsky (1970) discusses X-bar theory for sentences.
Using the contextual approximation theory of Perkins et al. (2014) they handtagged partexts at the paragraph level to get a validation set and used a rule-based
mapping to assign vocabulary contexts to semantic contexts. The assignments were
evaluated against the hand-tagging to determine the accuracy of the theory.
3
CHAPTER III
AN OVERVIEW OF TRANSFORMATION-BASED
ERROR-DRIVEN LEARNING
In Transformation-Based Error-Driven Learning and Natural Language
Processing: A Case Study in Part-of-Speech Tagging Brill (1995) demonstrates a method
that reveals the order that rules should be applied to a sentence to accurately tag each
word with the correct part of speech.
The algorithm used, originally described in A simple rule-based part of speech
tagger (Brill, 1992), was determined to be “… a useful tool for further exploring
linguistic modeling and attempting to discover ways of more tightly coupling the
underlying linguistic systems and our approximating models” (Brill, 1995, p. 544).
The method described shows that an unannotated text could be assigned an initial
state. A set of rules could then be processed one at the time on the original text. The
result of each rule when applied to the original text is then compared to a hand-tagged
copy of the original text that has been agreed upon to be correct, and the rule that
produced the most correct tags would be accepted as the first rule to be applied. The
remaining rules would then be applied to the text that has now been tagged by the first
rule. The result of the second round of tagging would be compared as before to the handtagged copy of the original text that has been agreed upon to be correct. The rule
producing the most correct tags would be accepted as the second rule to be applied. The
process would be repeated as many times as at least one of the rules contributed an
increase in correct tags.
When the process has completed, a related text can be set to the initial state and
processed using the rule order determined by the learner.
4
CHAPTER IV
METHODOLOGY
In Brill (1995) they gave the following description of how the information
workflow progresses during transformation-based error-driven learning (TBED).
Figure 1. Transformation-Based Error-Driven Learning.
In Perkins et al. (2014), the research also begins with an unannotated text. In the
model described in Brill (1995) the context of each word (noun, verb, adjective, adverb,
etc.) found in the unannotated text is to be determined at the sentence level. Each word is
limited to only one context, and each word must have a context. For P-bar theory, the
context of each word is based on if the word is found in one of the context dictionaries
and the unannotated text is taken at the paragraph level. If the word is found in more
than one dictionary, it will count toward the context for each dictionary, and it is possible
that some words will not contribute to a dictionary context and will be considered as no
context.
In transformation-based error-driven learning, each word in the unannotated text
has its initial state assigned to be a noun. With P-bar theory, the initial state of each word
5
is considered to be no context. In Brill (1995) the purpose is to get the correct context for
each word by applying rules to each sentence. In Perkins et al. (2014) they assign the
context of each word based on whether or not it appears in one of the context
dictionaries. The top of the combined workflow will now look like the following.
Figure 2. Addition of Context Dictionary for initial state.
In both Brill (1995) and Perkins et al. (2014), in order to gauge the accuracy of
each process, there has to be a text that has been hand-tagged and verified by one or more
people to be correct. In Brill (1995), this was identified as “truth,” and in Perkins et al.
(2014), it is identified as being the actual or correct context. This is the file that the
learner will use to verify if a rule can be successfully applied to the annotated text to
derive the correct context. The learner compares the annotated text with the known rules
to determine which rule, if any, will identify the correct tagging of words in sentences in
Brill (1995), or correct partext context in Perkins et al. (2014).
In Brill (1995), the learning process would take the annotated text, apply a rule,
and compare the number of correct word tagging with that of the “truth” text. It would
do this for each rule, and the rule that correctly identified and tagged the most words
correctly was identified to be the first rule. The results of the rule are applied to the
annotated text, and the process repeats until there are no more rules that generate
successful tags. P-bar theory can be mapped using the same process. In the annotated
6
text, the words will have been tagged based on the context dictionary or dictionaries in
which the word appeared. The rules use these tags, and their quantity and location, to
determine if there is a successful match to the actual context. The learner compares the
annotated text to each paragraph, and the rules are compared to determine if there is a
match. The rule with the most correct matches is considered the first rule. The results of
the rule are applied to the annotated text, and the process repeats until there are no more
rules that generate successful tags.
In both the TBED learner and the adapted P-bar TBED learner, the result is a set
of rules in the order that will produce the largest quantity of correct results. The diagram
below shows the adapted P-bar TBED workflow.
Figure 3. P-bar Translation-Based Error-Driven Learning.
7
CHAPTER V
LOGIC
During the process of translating P-bar theory into a TBED learning process,
certain decisions about how things would be done needed to be made. We have included
this information, so others interested would know what has been done.
In P-bar theory individuals were hand counting paragraphs, and at times their
counts would vary. Two programs were made. The first takes the text file and removes
as much anomaly data as can be found, such as extra line breaks, odd formatting, etc.,
and leaves a clean text file. The second program counts and labels each paragraph
identified in the text. From this counted text file each individual is able to make an actual
context file knowing that their paragraph identification will match that of the automated
learner.
In P-bar theory individuals did not always get the dictionary tagging accurate. As
seen in the data, some dictionary words did not get tagged. Term Tagger, Rishel
(2013) is a program that scans a dictionary and tags each word in the original text. With
permission, we integrated the logic from this code into the learner. This allowed for the
precise tagging of each word in each dictionary to the original text. As an example, if the
word “Mississippi” appears in a dictionary of states, and “Mississippi River” appears in a
dictionary of geography, the tagging would show:
Mississippi <state><geography> River <geography>
in the tagged text.
The individual can then supply the learner with the text to be processed, the file
containing the information they have determined to be the actual or correct context, and
the dictionary files. The learner then processes the information and provides the
8
individual with a file showing the rule order that obtained the highest number of correct
matches, a file showing the dictionary word count of each paragraph and the context that
each rule was able to identify for that paragraph, a file will a complete list of words from
the original text, and information as to the average sentence length, and a file showing the
original text tagged by the dictionaries.
The learner uses the following rules to evaluate the results:
1. The first occurrence of the context in the partext is the context.
2. The most occurrences of a context that reach the most count first is the
context.
3. The most occurrences of a context that reach the most count first and
match the first occurrence is the context.
4. The first context that appears three or more times in a row is the context.
5. The last context that appears three or more times in a row is the context.
9
CHAPTER VI
DATA ANALYSIS
Brill (1995) states that in their controlled test they were able to achieve successful
tagging as high as 95% compared to the control corpus. They stated that in their real
world test, using a sample from The Wall Street Journal, an accuracy rate of 85% was
obtained. For our comparative purposes, we will use the 85% accuracy rate as our
standard of measure.
In Perkins et al. (2014), individuals were given text from the Sir Arthur Conan
Doyle writings based on the character Sherlock Holmes, two chapters from the book The
Storm by Ivor van Heerden, and various chapters from the Harry Potter book series by
J.K. Rowling. The results from each individual for each story were combined. The
summary of the Sherlock Holmes stories is below. The complete data Tables for the
Sherlock Holmes stories are provided in Appendix A.
Table 1
Sherlock Holmes – Short Story Averages – P-bar Data Points
Title
Adventures of Copper Beaches
Engineers Thumb
Noble Bachelor
Orange Pips
Read Headed League
Resident Patient
Total Data Points
Invalid Data Points
% Valid Data Points
90
80
57
94
76
75
27
0
0
35
4
10
66
100
100
63
95
87
10
Table 1 (continued).
Title
Total Data Points
Invalid Data Points
% Valid Data Points
Speckled Band
The Blue Carbuncle
The Boscombe Valley Mystery
The Yellow Face
85
79
116
67
7
1
5
0
90
99
96
100
Averages
82
9
90
Table 2
Sherlock Holmes – Short Story Averages – P-bar Confidence Levels
Title
Human Confidence
P-Bar Confidence
P-Bar Context Match
Adventures of Copper Beaches
Engineers Thumb
Noble Bachelor
Orange Pips
Read Headed League
Resident Patient
Speckled Band
The Blue Carbuncle
The Boscombe Valley Mystery
The Yellow Face
57
89
93
51
66
71
76
93
89
96
57
69
63
50
50
39
67
68
44
51
100
78
68
98
76
55
88
73
49
53
Averages
78
56
71
A data point was considered invalid if there were no two individuals who agreed
on a context. Human confidence levels were calculated using the following scale. If all
11
three individuals agreed on a context for a paragraph, it was given 100%. If two of the
three individuals agreed on a context for a paragraph it was given 67%. If there was no
agreement on a context or if a data error was recognizable, the confidence was given 0%.
The P-bar Confidence levels were calculated using the same formula. The information in
the data Table summary for the short story, “The Red Headed League,” is read as
follows: There are 76 total data points. Of the 76 total data points, there are four that
have verifiable errors. This gives 95% of the data points to be considered accurate. The
individuals reporting information for the data points agreed on a context 66% of the time.
The individuals calculated P-bar Theory and believed it matched the correct context 50%
of the time.
If individuals can only come to an agreement on 66% of the text context and Pbar theory, can predict 50% of the text context, then P-bar theory matches the combined
individual context 76% of the time:
𝐶𝑜𝑛𝑡𝑒𝑥𝑡 𝑀𝑎𝑡𝑐ℎ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 =
𝑃 − 𝐵𝑎𝑟 𝑀𝑎𝑡𝑐ℎ
𝑥 100
𝐻𝑢𝑚𝑎𝑛 𝑀𝑎𝑡𝑐ℎ
The data shows that when more than one individual reads a selection of text the likelyhood of them coming to the same conclusion as to what the context of the text will
decrease. By applying P-bar theory and TBED learning to the same story, we get the
following table:
12
Table 3
Red Headed League – Rule Summary Data
P-bar theory was able to identify, count, and tag 217 paragraph level items in the story.
By processing rule four, rule three, rule five, and rule one, in that order P-bar was able to
correctly match the actual context file 186 times or 86% of the time. P-bar theory applied
in this way was able to predict 100% context for a best case, 49% for worst case, and an
average case of 75%. P-bar and TBED learning had a high of 86%, a low of 61%, and an
average of 78% for the same Sherlock Holmes stories as P-bar manually implemented.
This is a 7% increase.
The P-bar TBED learning process was also applied to sample chapters of The
Storm and also to sample chapters of the Harry Potter book series. The results are
equivalent in range to what was observed in the Sherlock Holmes short stories.
Representative summaries are provided in Appendix C.
13
The data collected shows that P-bar theory, when used on its own or with TBED
learning, is dependent on the perception and accuracy of the reader when they determine
the actual or correct context. The following data point from The Storm shows a
comparison from an individual with an engineering background (Person 1) and from a
person with more social and political awareness (Person 2).
Table 4
The Storm – Individual Comparison – Perception
Person 1
Person 2
Rule # # Correct Rule # # Correct
2
63
2
29
5
11
5
12
4
2
1
7
1
1
4
2
0
3
1
# Poss # Correct # Poss # Correct
92
77
92
51
% Correct
84
% Correct
55
There is no way to say that one is right and the other is wrong. It is only to be
said that even though the two individuals read the same material, each perception of the
context would be different. P-bar is a mechanical process that is not capable of detecting
nuance or inferred tone at this time. The rules used were able to match the context of the
first individual 84% of the time and the second individual 55% of the time. P-bar was
able to apply the rule order differently to adapt to the individuals’ perception of the text.
14
One of the things that we looked for during this process was to see if a pattern for
the optimal rule order would show up. It did not. There is a similarity in the pattern that
shows up but never a time where one pattern appeared as a dominate pattern.
Data examples for P-bar theory are provided in Appendix A, P-bar with TBED
learning in Appendix B, and P-bar with TBED summaries in Appendix C.
15
CHAPTER VII
CONCLUSION
We improved the accuracy of the P-bar theory rule set using a TransformationBased Error-Driven supervised learner model. The result was 80% accurate, an increase
of 8% over P-bar theory alone.
We demonstrated that P-bar with TBED was able to adapt to and improve the
results when a human reader bias was present in the data.
The choice and content of which dictionaries to use could have been made a
different way. This is an open problem. Additional work may be feasible to determine if
there exists an optimal form for the dictionaries.
Removing the human element from the context disambiguation reduces our
dependence on hand-tagged data while concurrently reducing bias in the result.
16
APPENDIX A
P-BAR DATA
How to read the appendix data. The first column labeled “Paragraph” is the
paragraph number as identified by the individual when the text was hand-tagged. The
next few columns represent the context options based on the number of dictionaries used.
For example, if there are four dictionaries there will be four columns, six dictionaries, six
columns, etc., up to a total of eight columns. The column header will be the name of the
context. The number in the column will be the total number of words in the paragraph
that match that context. The column named “Semantic” represents the context that the
individual believes is the actual context for the paragraph. In the event that no context
was identified, the individual usually left it blank. The column named “# Agree” is the
number of individuals who were in agreement as to the context of the paragraph. The
column named “Human Confidence” is a percentage calculated as described previously.
The column named “P-bar Theory” will contain a 1 if the individual believed P-bar
theory correctly matched the context in the “Semantic” column and 0 if it did not. The “#
Correct” column is a total from the “P-bar Theory” column. The “P-bar Confidence”
column is the percentage calculated as described previously.
17
Table A1
Sherlock Holmes – Blue Carbuncle - Combined P-bar Word Tagging
Paragraph weather army health deductions drugs crime england london india medicine location
3
3
3
5
5
5
6
6
6
7
7
7
9
9
9
13
13
13
17
17
17
25
25
25
27
27
27
29
29
29
31
31
31
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
2
2
2
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
2
2
2
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
35
35
35
36
36
36
37
37
37
39
39
39
40
40
40
41
41
41
43
43
43
45
45
45
48
48
48
51
51
51
52
52
52
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
3
3
3
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
0
0
0
1
1
1
1
1
1
19
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
56
56
56
58
58
58
60
60
60
61
61
61
62
62
62
68
68
68
72
72
72
73
73
73
80
80
80
81
81
81
82
82
82
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
3
3
3
1
1
1
3
3
3
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
0
0
0
2
2
2
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
2
2
2
1
1
1
0
0
0
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
0
0
0
1
1
1
3
3
3
0
0
0
1
1
1
1
1
1
0
0
0
1
1
1
2
2
2
20
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
83
83
83
85
85
85
87
87
87
94
94
94
95
95
95
96
96
96
101
101
101
113
113
113
114
114
114
123
123
123
124
124
124
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
2
2
2
0
0
0
0
0
0
1
1
1
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
21
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
125
125
125
128
128
128
130
130
130
131
131
131
135
135
135
140
140
140
141
141
141
143
143
143
144
144
144
146
146
146
151
151
151
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
22
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
152
152
152
153
153
153
154
154
154
160
160
160
166
166
166
168
168
168
174
174
174
177
177
177
182
182
182
183
183
183
184
184
184
1
1
1
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
23
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
186
186
186
187
187
187
188
188
188
189
189
189
190
190
190
191
191
191
192
192
192
193
193
193
195
195
195
196
196
196
202
202
202
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
1
1
1
1
1
1
1
1
1
2
2
2
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
2
2
2
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
3
2
2
2
1
1
1
0
0
0
1
1
1
0
0
0
24
Table A1 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
203
203
203
210
210
210
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
Table A2
Sherlock Holmes – Blue Carbuncle – Combined Confidence Levels
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
3
3
3
5
5
5
6
6
6
7
7
7
9
9
9
location
location
location
deduction
deductions
deductions
weather
locations
Crime
crime
crime
Deduction
deduction
deductions
Crime
3
100
3
100
0
0
2
67
2
67
1
1
1
1
1
1
1
0
0
0
0
0
1
1
0
3
100
3
100
1
0
0
0
2
67
25
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
13
13
13
17
17
17
25
25
25
27
27
27
29
29
29
31
31
31
35
35
35
36
36
36
37
37
37
39
39
39
crime
crime
Crime
deductions
deductions
deductions
deductions
deductions
deductions
deduction
deductions
Deduction
Crime
Crime
Crime
Deduction
Deduction
Deduction
deduction
deductions
deductions
deduction
deductions
deductions
Location
Location
Location
deduction
deduction
Deduction
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
0
0
0
1
1
1
0
0
0
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
0
0
0
1
1
1
0
0
3
100
0
0
3
100
3
100
2
67
3
100
3
100
0
0
3
100
26
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
40
40
40
41
41
41
43
43
43
45
45
45
48
48
48
51
51
51
52
52
52
56
56
56
58
58
58
60
60
60
deduction
deductions
deductions
deductions
deductions
Deductions
weather
weather
weather
location
location
location
crime
crime
Crime
deduction
deductions
deduionsct
location
location
location
England
England
England
Deduction
Deduction
Deduction
crime
crime
Crime
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
1
1
1
1
1
1
0
1
0
1
1
0
1
1
0
1
1
1
1
1
1
1
0
0
0
0
1
1
1
1
3
100
3
100
1
0
2
67
2
67
3
100
3
100
1
0
1
0
3
100
27
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
61
61
61
62
62
62
68
68
68
72
72
72
73
73
73
80
80
80
81
81
81
82
82
82
83
83
83
85
85
85
crime
crime
Crime
deduction
crime
Deduction
deductions
deductions
Deduction
army
army
army
England
England
England
Deduction
crime
Deduction
deductions
deductions
Deduction
crime
Weather
Weather
weather
weather
weather
deductions
deductions
deductions
3
100
2
67
3
100
3
100
3
100
2
67
3
100
2
67
3
100
3
100
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
0
1
0
1
1
1
0
1
1
1
1
1
1
1
1
3
100
0
0
3
100
3
100
3
100
1
0
3
100
2
67
3
100
3
100
28
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
87
87
87
94
94
94
95
95
95
96
96
96
101
101
101
113
113
113
114
114
114
123
123
123
124
124
124
125
125
125
deductions
deductions
deductions
deductions
deductions
deductions
location
location
location
deductions
deductions
deductions
location
weather
Location
weather
deductions
Deduction
army
deductions
deductions
location
location
location
deductions
deductions
deductions
army
army
army
3
100
3
100
3
100
3
100
2
67
2
67
2
67
3
100
3
100
3
100
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
3
100
3
100
3
100
3
100
1
0
2
67
2
67
3
100
3
100
3
100
29
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
128
128
128
130
130
130
131
131
131
135
135
135
140
140
140
141
141
141
143
143
143
144
144
144
146
146
146
151
151
151
weather
weather
weather
deduction
deductions
Deduction
location
location
location
England
England
England
deductions
deductions
deductions
deduction
deductions
Deduction
England
England
England
london
location
Location
london
london
london
Weather
Weather
Weather
3
100
3
100
3
100
3
100
3
100
3
100
3
100
2
67
3
100
3
100
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
1
1
1
1
3
100
3
100
0
0
3
100
3
100
3
100
3
100
1
0
3
100
3
100
30
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
152
152
152
153
153
153
154
154
154
160
160
160
166
166
166
168
168
168
174
174
174
177
177
177
182
182
182
183
183
183
deduction
deduction
Deduction
Crime
Crime
Crime
deductions
deductions
deductions
Deduction
Deduction
Deduction
weather
weather
weather
crime
crime
Crime
army
army
army
location
location
location
Crime
Crime
Crime
drug
drug
drug
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
1
1
1
1
1
1
0
0
0
1
1
0
1
1
1
1
1
1
0
0
0
1
1
0
1
1
1
1
1
1
3
100
3
100
0
0
2
67
3
100
3
100
0
0
2
67
3
100
3
100
31
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
184
184
184
186
186
186
187
187
187
188
188
188
189
189
189
190
190
190
191
191
191
192
192
192
193
193
193
195
195
195
crime
crime
Crime
crime
crime
Deduction
crime
crime
Crime
crime
crime
crime
crime
crime
crime
crime
crime
Crime
crime
crime
Crime
crime
crime
England
crime
crime
England
deduction
deduction
deduction
3
100
2
67
3
100
3
100
3
100
3
100
3
100
2
67
2
67
3
100
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
1
0
0
1
1
1
1
0
0
2
67
3
100
0
0
0
0
3
100
0
0
1
0
1
0
3
100
32
Table A2 (continued).
Paragraph Semantict # Agree Human Confidence P-bar Theory # Correct P-Bar Confidence
196
196
196
202
202
202
203
203
203
210
210
210
location
location
location
crime
crime
crime
Crime
deductions
Crime
location
location
location
3
100
3
100
2
67
3
100
1
1
1
0
0
0
1
0
1
1
1
1
3
100
0
0
2
67
3
100
Table A3
Sherlock Holmes – Boscombe Valley – Combined P-bar Word Tagging
Paragraph weather army health deductions drugs crime england london india medicine location
Paragraph weather army health deductions drugs crime england london india medicine location
2
1
1
0
1
0
0
0
2
0
0
1
2
1
1
1
1
1
1
1
1
1
1
1
2
1
1
0
1
0
0
0
2
0
0
1
7
0
1
0
0
0
0
0
1
0
1
0
7
0
1
0
0
0
0
0
1
0
1
0
7
0
1
0
0
0
0
0
1
0
1
0
12
0
0
0
1
0
0
0
1
0
0
0
12
0
0
0
1
0
0
0
1
0
0
0
12
0
0
0
1
0
0
0
1
0
0
0
33
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
14
14
14
16
16
16
17
17
17
18
18
18
19
19
19
20
20
20
22
22
22
23
23
23
24
24
24
26
26
26
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
5
5
5
6
6
6
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
4
4
4
4
4
4
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
1
1
1
2
2
2
3
3
3
3
3
3
0
0
0
0
0
0
1
1
1
34
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
28
28
28
32
32
32
37
37
37
38
38
38
39
39
39
42
42
42
43
43
43
47
47
47
56
56
56
60
60
60
0
0
0
0
0
0
0
0
0
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
1
1
1
2
2
2
3
3
3
1
1
1
1
1
1
0
0
0
1
1
1
2
2
2
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
0
0
0
5
5
5
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
35
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
67
67
67
68
68
68
69
69
69
70
70
70
71
71
71
72
72
72
74
74
74
75
75
75
76
76
76
77
77
77
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
7
7
7
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
4
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
36
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
78
78
78
79
79
79
83
83
83
84
84
84
85
85
85
86
86
86
87
87
87
88
88
88
89
89
89
90
90
90
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
92
92
92
100
100
100
102
102
102
104
104
104
107
107
107
109
109
109
115
115
115
117
117
117
118
118
118
119
119
119
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
3
3
3
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
0
0
0
1
1
1
0
1
1
8
8
8
0
0
0
1
1
1
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
7
7
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
8
8
8
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
38
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
121
121
121
124
124
124
129
129
129
130
130
130
131
131
131
132
132
132
133
133
133
135
135
135
136
136
136
137
137
137
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
3
3
3
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
3
3
3
1
1
1
1
1
1
2
2
2
1
1
1
0
0
0
0
0
0
2
2
2
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
3
3
3
4
4
4
0
0
0
6
6
6
1
1
1
39
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
138
138
138
139
139
139
143
143
143
146
146
146
147
147
147
148
148
148
157
157
157
158
158
158
160
160
160
161
161
161
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
3
3
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
1
1
0
1
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
2
2
0
2
0
40
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
162
162
162
163
163
163
164
164
164
165
165
165
167
167
167
169
169
169
170
170
170
171
171
171
173
173
173
174
174
174
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
1
1
1
0
0
0
3
0
3
2
0
0
0
0
2
0
2
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
1
0
0
0
0
1
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
2
0
0
0
0
0
0
0
2
1
2
0
0
2
0
0
0
1
41
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
175
175
175
177
177
177
180
180
180
181
181
181
182
182
182
183
183
183
184
184
184
185
185
185
186
186
186
187
187
187
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
2
0
2
1
0
1
0
0
0
0
0
2
0
2
1
0
0
0
1
2
1
0
0
0
1
1
1
0
0
0
2
0
2
2
0
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
2
0
0
0
0
0
1
0
0
0
0
1
0
1
0
0
0
0
0
0
1
0
1
0
0
0
0
42
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
188
188
188
189
189
189
190
190
190
191
191
191
192
192
192
194
194
194
197
197
197
199
199
199
200
200
200
201
201
201
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
2
0
0
1
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
1
0
0
0
0
1
2
1
0
2
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
1
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
43
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
202
202
202
203
203
203
204
204
204
205
205
205
206
206
206
207
207
207
208
208
208
209
209
209
210
210
210
211
211
211
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
1
0
2
0
0
0
0
0
0
0
1
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
0
1
0
1
0
1
0
0
0
1
0
1
1
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
2
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
2
0
2
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
1
1
0
1
0
2
0
0
0
0
0
0
0
1
1
1
1
1
1
0
1
0
0
0
0
0
0
0
1
0
1
1
0
1
1
0
1
0
0
0
1
0
1
0
0
0
0
1
0
0
1
0
0
44
Table A3 (continued).
Paragraph weather army health deductions drugs crime england london india medicine location
212
212
212
213
213
213
214
214
214
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
1
0
0
Table A4
Sherlock Holmes – Boscombe Valley - Combined Confidence Levels
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
2
2
2
7
7
7
12
12
12
london
location
london
london
location
location
crime
crime
Crime
3
100
2
67
3
100
1
0
1
0
0
0
0
0
0
2
67
0
0
0
0
45
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
14
14
14
16
16
16
17
17
17
18
18
18
19
19
19
20
20
20
22
22
22
23
23
23
24
24
24
26
26
26
crime
crime
Crime
Deduction
location
Deduction
location
England
England
location
location
location
crime
crime
Crime
crime
crime
Deduction
crime
crime
Crime
crime
crime
Crime
Crime
Crime
Crime
army
army
Deduction
3
100
2
67
2
67
3
100
3
100
2
67
3
100
3
100
3
100
2
67
1
1
1
0
1
0
1
0
0
0
0
1
0
0
0
1
0
1
1
0
1
0
0
0
0
0
0
1
1
0
3
100
1
0
1
0
1
0
0
0
2
67
2
67
0
0
0
0
2
67
46
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
28
28
28
32
32
32
37
37
37
38
38
38
39
39
39
42
42
42
43
43
43
47
47
47
56
56
56
60
60
60
deduction
deduction
Deduction
crime
crime
Crime
crime
crime
Deduction
crime
deduction
Crime
Deduction
Deduction
Deduction
Deduction
Deduction
Deduction
deductions
locations
locations
crime
crime
Crime
crime
crime
Deduction
deduction
deduction
Deduction
3
100
3
100
2
67
2
67
3
100
3
100
2
67
3
100
2
67
3
100
1
1
1
1
0
1
0
0
1
1
1
0
1
1
1
1
1
1
0
1
1
1
1
0
0
0
1
1
1
1
3
100
2
67
1
0
2
67
3
100
3
100
2
67
2
67
1
0
3
100
47
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
67
67
67
68
68
68
69
69
69
70
70
70
71
71
71
72
72
72
74
74
74
75
75
75
76
76
76
77
77
77
deduction
deduction
Deduction
deduction
deduction
Crime
deduction
deduction
Deduction
crime
location
location
england
crime
location
crime
location
crime
weather
weather
weather
weather
crime
Deduction
crime
crime
crime
deduction
crime
Crime
3
100
2
67
3
100
2
67
0
0
2
67
3
100
0
0
3
100
2
67
1
1
1
1
1
0
1
1
1
0
0
1
1
0
1
0
1
0
1
1
1
1
1
1
0
0
0
1
0
0
3
100
2
67
3
100
1
0
0
0
1
0
3
100
0
0
0
0
1
0
48
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
78
78
78
79
79
79
83
83
83
84
84
84
85
85
85
86
86
86
87
87
87
88
88
88
89
89
89
90
90
90
crime
deductions
crime
deduction
crime
crime
deduction
deduction
Deduction
deduction
deduction
deduction
deduction
deduction
deduction
Crime
Crime
Crime
deduction
deduction
Deduction
deduction
deduction
deduction
deduction
deduction
deduction
deduction
deduction
deduction
2
67
2
67
3
100
3
100
3
100
2
67
3
100
3
100
3
100
3
100
0
1
0
1
0
0
1
1
1
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
0
1
0
3
100
3
100
0
0
0
0
3
100
0
0
0
0
0
0
49
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
92
92
92
100
100
100
102
102
102
104
104
104
107
107
107
109
109
109
115
115
115
117
117
117
118
118
118
119
119
119
health
health
Health
health
health
location
deduction
deduction
location
deduction
deduction
Deduction
crime
crime
Crime
weather
weather
weather
deduction
deduction
Crime
crime
crime
Crime
health
location
weather
health
health
medicine
3
100
2
67
2
67
3
100
3
100
3
100
2
67
3
100
0
0
2
67
0
0
0
0
0
1
1
0
1
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
1
1
0
1
1
0
0
0
1
0
2
67
3
100
0
0
3
100
0
0
0
0
0
0
2
67
50
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
121
121
121
124
124
124
129
129
129
130
130
130
131
131
131
132
132
132
133
133
133
135
135
135
136
136
136
137
137
137
medicine
medicine
medicine
deduction
crime
Deduction
crime
crime
Crime
deductions
deductions
deductions
crime
crime
crime
location
location
location
location
location
location
deduction
deduction
deduction
deduction
crime
location
deduction
deduction
Deduction
3
100
2
67
3
100
3
100
3
100
3
100
3
100
3
100
0
0
3
100
1
1
1
0
1
0
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
1
0
0
1
1
1
3
100
1
0
3
100
3
100
3
100
3
100
0
0
0
0
1
0
3
100
51
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
138
138
138
139
139
139
143
143
143
146
146
146
147
147
147
148
148
148
157
157
157
158
158
158
160
160
160
161
161
161
England
England
England
crime
crime
Crime
location
location
location
England
England
England
london
london
london
crime
crime
crime
crime
crime
crime
drug
drug
drug
deduction
deduction
Deduction
deduction
deduction
deduction
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
0
0
0
1
1
1
1
1
1
0
0
0
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
0
0
3
100
3
100
0
0
3
100
0
0
3
100
3
100
3
100
3
100
52
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
162
162
162
163
163
163
164
164
164
165
165
165
167
167
167
169
169
169
170
170
170
171
171
171
173
173
173
174
174
174
Deduction
Deduction
Deduction
crime
crime
crime
crime
crime
crime
england
england
england
crime
crime
crime
deductions
deductions
deductions
Deduction
Deduction
Deduction
location
location
location
location
location
location
Deduction
Deduction
Deduction
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
0
0
0
0
0
0
1
1
1
1
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
3
100
3
100
2
67
0
0
0
0
0
0
1
0
1
0
53
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
175
175
175
177
177
177
180
180
180
181
181
181
182
182
182
183
183
183
184
184
184
185
185
185
186
186
186
187
187
187
deduction
deduction
deduction
deduction
deduction
deduction
Deduction
Deduction
Deduction
deduction
deduction
deduction
Drugs
Drugs
Drugs
deduction
drug
deduction
Deduction
Deduction
Deduction
drug
drug
Deduction
crime
crime
crime
deduction
deduction
deduction
3
100
3
100
3
100
3
100
3
100
2
67
3
100
2
67
3
100
3
100
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
0
0
1
1
1
0
0
0
0
0
0
1
1
1
3
100
3
100
3
100
3
100
0
0
1
0
3
100
0
0
0
0
3
100
54
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
188
188
188
189
189
189
190
190
190
191
191
191
192
192
192
194
194
194
197
197
197
199
199
199
200
200
200
201
201
201
deduction
deduction
deduction
health
health
health
Deduction
Deduction
Deduction
deduction
deduction
deduction
deduction
deduction
deduction
health
health
health
deduction
deduction
deduction
Health
Health
Health
health
health
health
crime
crime
crime
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
3
100
0
0
0
0
0
0
3
100
3
100
3
100
0
0
55
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
202
202
202
203
203
203
204
204
204
205
205
205
206
206
206
207
207
207
208
208
208
209
209
209
210
210
210
211
211
211
crime
crime
crime
England
England
England
Deduction
Deduction
Deduction
crime
crime
crime
health
health
health
deduction
deduction
deduction
crime
crime
crime
Crime
Crime
Crime
deduction
deduction
deduction
crime
deduction
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
3
100
0
0
1
1
1
0
0
0
0
0
0
1
0
1
1
0
0
1
1
1
0
1
0
0
0
0
0
0
0
0
1
3
100
0
0
0
0
2
67
1
0
3
100
1
0
0
0
0
0
1
0
56
Table A4 (continued).
Paragraph Semantic # Agree Human Confidence P-bar theory # Correct P-BarConfidence
212
212
212
213
213
213
214
214
214
england
england
Deduction
deduction
deduction
deduction
crime
crime
crime
2
67
3
100
3
100
1
1
0
1
1
1
1
1
1
2
67
3
100
3
100
Table A5
Sherlock Holmes – Cooper Beaches – Word Count Data
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
1
1
1
2
2
2
3
3
3
5
5
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
2
2
2
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
57
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
6
6
6
7
7
7
9
9
9
10
10
10
11
11
11
16
16
16
17
17
17
19
19
19
20
20
20
22
22
22
23
23
23
2
2
2
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
2
2
2
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
0
0
0
1
0
1
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
58
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
25
25
25
26
26
26
31
31
31
32
32
32
34
34
34
36
36
36
37
37
37
39
39
39
41
41
41
44
44
44
50
50
50
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
1
1
0
1
0
1
0
0
0
3
0
3
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
59
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
52
52
52
53
53
53
56
56
56
60
60
60
63
63
63
68
68
68
71
71
71
72
72
72
74
74
74
75
75
75
76
76
76
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
1
1
1
0
0
0
4
4
4
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
1
1
1
0
1
1
0
1
0
0
0
2
0
2
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
80
80
80
82
82
82
83
83
83
91
91
91
92
92
92
93
93
93
95
95
95
96
96
96
97
97
97
99
99
99
104
104
104
0
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
1
1
1
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
1
2
2
2
2
2
2
1
1
1
0
0
0
0
0
0
1
1
2
0
0
0
0
0
0
4
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
1
1
1
1
2
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
6
0
6
1
1
0
0
0
0
3
3
3
1
1
1
2
2
0
0
0
0
0
0
0
3
0
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
61
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
107
107
107
108
108
108
109
109
109
111
111
111
112
112
112
113
113
113
115
115
115
116
116
116
117
117
117
118
118
118
119
119
119
0
0
0
0
0
0
1
1
0
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
1
1
0
0
0
0
3
3
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
4
4
0
2
2
0
0
0
0
0
0
0
1
1
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
6
6
0
2
2
0
3
3
0
2
2
0
0
0
0
0
0
0
4
4
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
62
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
120
120
120
123
123
123
129
129
129
132
132
132
133
133
133
134
134
134
135
135
135
136
136
136
137
137
137
141
141
141
142
142
142
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
0
0
0
0
1
1
0
0
0
0
1
1
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
0
1
1
0
2
2
0
1
1
0
3
3
0
1
1
0
0
0
0
1
1
0
3
3
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
143
143
143
144
144
144
145
145
145
157
157
157
168
168
168
170
170
170
171
171
171
172
172
172
175
175
175
176
176
176
178
178
178
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
1
1
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
3
3
0
2
2
0
2
2
0
0
0
0
0
0
0
1
1
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
3
3
0
2
2
0
1
1
0
0
0
0
4
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
64
Table A5 (continued).
Paragraph Weather Army Health Deduction Drug Crime England London Location India Medicine
182
182
182
183
183
183
184
184
184
189
189
189
191
191
191
196
196
196
198
198
198
199
199
199
201
201
201
203
203
203
207
207
207
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0
3
3
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
2
2
0
1
1
0
1
1
0
2
2
0
1
1
0
3
3
0
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
65
Table A6
Sherlock Holmes – Cooper Beaches – P-bar Confidence Levels
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
1
1
1
2
2
2
3
3
3
5
5
5
6
6
6
7
7
7
9
9
9
10
10
10
11
11
11
16
16
16
Cd
Cd
cd
Ca
3
100
1
0
Cd
Cd
cd
Cc
Cc
cc
Cw
Cw
cw
Cw
Cw
cd
Cd
Cd
cd
Clo
clo
3
100
3
100
3
100
2
67
3
100
2
67
Clo
clo
2
67
Ce
Ce
cd
2
67
1
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
0
1
1
0
1
1
0
3
100
0
0
3
100
3
100
3
100
2
67
3
100
2
67
2
67
2
67
66
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
17
17
17
19
19
19
20
20
20
22
22
22
23
23
23
25
25
25
26
26
26
31
31
31
32
32
32
34
34
34
Cd
Cd
cd
Cd
Cd
cd
Clo
clo
3
100
3
100
2
67
Ca
Ca
ca
Cd
Cd
cd
Clo
cd
3
100
3
100
0
0
Clo
cd
0
0
Ca
Ca
ca
Cw
3
100
1
0
Clo
Cd
cd
2
67
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
0
0
0
0
0
1
1
1
1
0
0
1
1
1
3
100
3
100
2
67
3
100
3
100
0
0
0
0
3
100
0
0
2
67
67
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
36
36
36
37
37
37
39
39
39
41
41
41
44
44
44
50
50
50
52
52
52
53
53
53
56
56
56
60
60
60
Ca
Ca
ca
Ca
Ca
cd
Clo
clo
3
100
2
67
2
67
Cc
Cc
cc
Cd
Cd
cd
Cd
Cd
ch
Cc
cd
3
100
3
100
2
67
0
0
Ch
Ch
cd
Cd
Cd
cd
Clo
Cl
cl
2
67
3
100
2
67
1
1
1
1
1
0
1
1
0
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
3
100
2
67
2
67
3
100
3
100
2
67
0
0
2
67
3
100
2
67
68
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
63
63
63
68
68
68
71
71
71
72
72
72
74
74
74
75
75
75
76
76
76
80
80
80
82
82
82
83
83
83
Cd
Cd
cd
Clo
Ca
clo
Cd
Cd
cd
Cd
Cc
cd
Cd
Cd
cd
Cd
Cd
cd
Cd
cd
3
100
2
67
3
100
2
67
3
100
3
100
2
67
Ch
Ch
ch
Cd
Cd
cd
Cd
cd
Cd
3
100
3
100
3
100
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
3
100
2
67
3
100
2
67
3
100
3
100
2
67
3
100
3
100
3
100
69
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
91
91
91
92
92
92
93
93
93
95
95
95
96
96
96
97
97
97
99
99
99
104
104
104
107
107
107
108
108
108
Cd
Cd
cd
Cw
clo
Cw
Clo
clo
clo
Cd
cd
Cd
Cc
Cc
cc
Cl
Cl
cc
Cd
Cd
cd
Clo
3
100
2
67
3
100
3
100
3
100
2
67
3
100
1
0
Ce
Ca
0
0
Ca
ca
2
67
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
0
1
1
0
3
100
2
67
3
100
3
100
3
100
2
67
3
100
0
0
0
0
2
67
70
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
109
109
109
111
111
111
112
112
112
113
113
113
115
115
115
116
116
116
117
117
117
118
118
118
119
119
119
120
120
120
Ch
clo
0
0
Clo
clo
2
67
Cd
cd
2
67
Clo
cd
0
0
Clo
clo
2
67
Clo
cm
0
0
Cd
ch
0
0
Clo
clo
2
67
Clo
clo
2
67
Clo
cd
0
0
0
1
0
1
1
0
1
1
0
1
1
0
1
1
0
0
1
0
1
1
0
1
1
0
1
1
0
1
1
0
0
0
2
67
2
67
0
0
2
67
0
0
0
0
2
67
2
67
0
0
71
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
123
123
123
129
129
129
132
132
132
133
133
133
134
134
134
135
135
135
136
136
136
137
137
137
141
141
141
142
142
142
Clo
clo
2
67
Clo
cd
0
0
Cm
cm
2
67
Cw
clo
0
0
Cd
cd
2
67
Cc
cc
2
67
Cd
cd
2
67
Clo
clo
2
67
cd
cd
2
67
Cd
cd
2
67
1
1
0
1
0
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
2
67
0
0
2
67
0
0
2
67
2
67
2
67
2
67
2
67
2
67
72
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
143
143
143
144
144
144
145
145
145
157
157
157
168
168
168
170
170
170
171
171
171
172
172
172
175
175
175
176
176
176
Cd
cd
2
67
Ca
clo
0
0
Cd
cd
2
67
Clo
cd
0
0
Cd
cd
2
67
Cm
cd
0
0
Cc
cc
2
67
Clo
cc
0
0
Clo
cd
0
0
Cd
cd
2
67
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
0
1
0
1
1
0
2
67
0
0
2
67
0
0
2
67
0
0
2
67
0
0
0
0
2
67
73
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
178
178
178
182
182
182
183
183
183
184
184
184
189
189
189
191
191
191
196
196
196
198
198
198
199
199
199
201
201
201
Cc
cd
0
0
Ch
cd
0
0
Clo
clo
2
67
Cc
clo
0
0
Cc
cd
0
0
Cd
clo
0
0
Clo
clo
2
67
Ch
cd
0
0
Cd
cd
2
67
Cl
cd
0
0
1
1
0
1
0
0
1
1
0
0
1
0
0
1
0
1
1
0
1
1
0
1
1
0
1
1
0
1
1
0
0
0
0
0
2
67
0
0
0
0
0
0
2
67
0
0
2
67
0
0
74
Table A6 (continued).
Paragraph Semantic # Agree Human Confidence P-Bar theory # Correct P-Bar Confidence
203
203
203
207
207
207
Ca
ca
2
67
Ch
clo
0
0
1
1
0
1
1
0
2
67
0
0
75
APPENDIX B
P-BAR WHEN APPLIED TO TBED LEARNER
How to read the appendix data. The first column labeled “Paragraph” is the
paragraph number as identified by the paragraph counter in the software. The next few
columns represent the context options based on the number of dictionaries used. For
example if there are four dictionaries there will be four columns, six dictionaries, six
columns, etc., up to a total of 8 columns. The column header will be the name of the
context. The number in the column will be the total number of words in the paragraph
that match that context. The following set of data will have a paragraph column and five
columns representing the rules used by the tagger in order. If a context for a paragraph
was able to be matched by a rule, that context will be identified in the paragraph row
under the corresponding rule column. The last column is labeled “actual” is the context
that was determined by the actual context file to be the correct context for the paragraph.
In the even that no context was identified by either a rule or the actual context file, the
cell is marked NC. An identification is considered correct when at least one rule matches
the actual context column.
Table B1
Sherlock Holmes – Blue Carbuncle - Word Count Data
Paragraph weather army deduction crime india location eng-lon hmd
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
76
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
2
0
0
2
1
0
1
0
0
0
4
0
0
0
2
0
0
0
0
0
0
0
0
1
1
0
0
4
0
2
0
0
0
0
0
1
0
0
0
0
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
1
0
1
0
1
0
0
0
0
0
1
1
1
1
0
0
0
2
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
1
1
2
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
77
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
0
0
0
0
1
1
0
2
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
0
0
1
1
4
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
2
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
3
0
1
0
1
1
0
0
0
0
1
1
0
0
0
0
1
0
2
0
0
2
3
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
2
0
0
0
0
0
0
2
3
0
2
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
78
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
0
2
0
0
1
1
4
0
0
0
1
0
0
1
0
0
4
0
1
0
0
0
0
1
0
1
0
0
0
2
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
3
0
2
0
0
0
0
0
0
0
0
1
0
0
0
2
0
0
0
0
2
0
0
0
0
0
0
1
0
1
1
0
2
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
1
1
1
3
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
79
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
0
0
2
0
0
0
0
0
0
0
0
1
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
80
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
2
3
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
81
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
0
0
0
1
0
1
0
0
0
0
2
2
0
1
0
0
0
0
1
1
0
1
0
1
0
2
0
0
3
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
2
0
1
0
2
2
1
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
2
0
1
0
2
2
1
1
1
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
4
2
2
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
3
4
4
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
2
1
1
0
1
0
1
0
0
1
0
0
0
82
Table B1 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
0
0
0
0
0
0
1
1
3
0
0
0
0
1
0
2
0
0
0
0
0
2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
2
83
Table B2
Sherlock Holmes – Blue Carbuncle – Rule Context Match
Paragraph
rule 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
NC
NC
weather
NC
location
army
crime
crime
deduction
NC
NC
NC
army
NC
NC
NC
deduction
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
weather
deduction
NC
weather
rule 2
rule 3
rule 4
rule 5
actual
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
NC
weather deduction location
NC
NC
NC
NC
NC
deduction
NC
location deduction deduction
weather
NC
army
weather weather
location
NC
crime
weather
crime
crime
crime
crime
crime
NC
deduction
NC
deduction weather deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
army
army
army
army
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
crime
NC
crime
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
NC
location weather
NC
weather weather
NC
NC
NC
deduction deduction
NC
NC
deduction
NC
NC
NC
NC
NC
weather
NC
weather
crime
NC
84
Table B2 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
NC
weather
NC
NC
NC
deduction
deduction
location
weather
weather
deduction
hmd
NC
weather
NC
location
location
weather
crime
NC
NC
hmd
eng-lon
NC
eng-lon
eng-lon
NC
location
NC
deduction
NC
crime
NC
hmd
NC
NC
NC
deduction
deduction
location
weather
weather
deduction
deduction
NC
weather
NC
location
location
weather
crime
NC
NC
hmd
eng-lon
NC
eng-lon
eng-lon
NC
location
NC
location
NC
crime
NC
NC
NC
NC
NC
deduction
deduction
location
weather
NC
deduction
NC
NC
NC
NC
NC
location
NC
crime
NC
NC
NC
NC
NC
eng-lon
eng-lon
NC
location
NC
NC
NC
NC
NC
weather
NC
NC
NC
deduction
deduction
location
weather
NC
deduction
hmd
NC
army
NC
army
location
weather
crime
NC
NC
hmd
eng-lon
NC
eng-lon
eng-lon
NC
location
NC
deduction
NC
crime
NC
weather
NC
NC
NC
deduction
deduction
location
weather
NC
deduction
weather
NC
army
NC
army
location
weather
crime
NC
NC
deduction
location
NC
eng-lon
eng-lon
NC
location
NC
location
NC
deduction
NC
NC
NC
NC
NC
deduction
deduction
NC
NC
deduction
deduction
NC
NC
NC
NC
location
NC
NC
crime
NC
NC
deduction
location
NC
NC
NC
NC
NC
NC
NC
NC
crime
85
Table B2 (continued).
Paragraph
rule 1
rule 2
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
crime
location
NC
eng-lon
NC
eng-lon
NC
army
hmd
eng-lon
eng-lon
NC
NC
NC
weather
NC
NC
crime
location
crime
crime
NC
crime
NC
deduction
NC
NC
hmd
NC
weather
NC
crime
crime
eng-lon
NC
eng-lon
NC
deduction
NC
army
hmd
eng-lon
weather
NC
NC
NC
weather
NC
NC
crime
location
location
weather
NC
deduction
NC
deduction
NC
NC
hmd
NC
weather
NC
crime
rule 3
rule 4
rule 5
actual
NC
crime
eng-lon
crime
location location location deduction
NC
NC
NC
NC
NC
eng-lon weather
NC
NC
NC
NC
NC
NC
weather
army
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
hmd
weather
NC
NC
eng-lon weather
NC
NC
eng-lon weather
NC
NC
NC
NC
NC
NC
NC
NC
crime
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction weather
NC
NC
deduction deduction
NC
NC
crime
location
NC
NC
crime
weather
NC
NC
NC
NC
crime
NC
crime
hmd
weather
NC
NC
NC
NC
deduction deduction deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
hmd
weather
NC
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
NC
crime
NC
NC
NC
86
Table B2 (continued).
Paragraph
rule 1
rule 2
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
location
weather
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
NC
NC
hmd
weather
eng-lon
hmd
hmd
NC
NC
NC
NC
NC
NC
NC
NC
location
crime
army
NC
location
weather
NC
NC
NC
NC
location
NC
NC
NC
NC
NC
NC
NC
NC
hmd
crime
location
hmd
hmd
NC
NC
NC
NC
NC
NC
NC
NC
location
crime
army
NC
rule 3
rule 4
rule 5
actual
location location location
NC
weather weather weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
army
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather location
NC
NC
weather weather
NC
NC
eng-lon deduction
NC
NC
hmd
weather
NC
NC
deduction
army
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
NC
crime
crime
crime
NC
army
army
army
location
NC
NC
NC
NC
87
Table B2 (continued).
Paragraph
rule 1
rule 2
rule 3
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
NC
weather
weather
deduction
eng-lon
NC
NC
NC
deduction
NC
NC
NC
NC
deduction
deduction
NC
hmd
eng-lon
NC
eng-lon
NC
NC
NC
NC
NC
weather
hmd
hmd
crime
army
NC
NC
NC
weather
weather
deduction
eng-lon
NC
NC
NC
deduction
NC
NC
NC
NC
deduction
deduction
NC
hmd
eng-lon
NC
eng-lon
NC
NC
NC
NC
NC
weather
deduction
hmd
crime
army
NC
NC
NC
weather
NC
deduction
eng-lon
NC
NC
NC
NC
NC
NC
NC
NC
deduction
deduction
NC
NC
eng-lon
NC
eng-lon
NC
NC
NC
NC
NC
NC
NC
NC
NC
army
NC
NC
rule 4
rule 5
actual
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction weather
NC
eng-lon
eng-lon
NC
NC
NC
deduction
NC
NC
location
NC
NC
NC
deduction eng-lon
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction deduction
NC
deduction deduction
NC
NC
NC
NC
hmd
weather deduction
eng-lon
eng-lon
NC
NC
NC
NC
eng-lon
eng-lon
eng-lon
NC
NC
NC
NC
NC
eng-lon
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
army
NC
deduction weather
NC
deduction weather
NC
location location deduction
army
army
NC
NC
NC
NC
NC
NC
NC
88
Table B2 (continued).
Paragraph
rule 1
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
deduction
eng-lon
NC
NC
NC
NC
NC
weather
NC
crime
NC
NC
NC
NC
crime
army
NC
deduction
location
NC
NC
NC
army
army
hmd
hmd
NC
hmd
crime
weather
army
crime
rule 2
rule 3
rule 4
rule 5
actual
deduction deduction
NC
NC
NC
eng-lon
NC
eng-lon weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
crime
NC
crime
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
NC
crime
weather
NC
weather
NC
weather weather
NC
NC
NC
NC
NC
NC
deduction
NC
deduction weather
NC
location location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
NC
NC
NC
army
NC
army
weather
NC
crime
NC
crime
hmd
NC
hmd
hmd
hmd
hmd
NC
hmd
NC
hmd
eng-lon
NC
NC
NC
NC
NC
NC
crime
NC
hmd
deduction crime
crime
crime
crime
crime
NC
weather
NC
weather
crime
crime
army
NC
army
army
crime
deduction
NC
crime deduction crime
89
Table B2 (continued).
Paragraph
rule 1
rule 2
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
eng-lon
eng-lon
eng-lon
NC
deduction
location
NC
NC
NC
NC
weather
hmd
eng-lon
NC
NC
NC
NC
weather
NC
weather
NC
NC
NC
NC
location
weather
location
eng-lon
eng-lon
NC
deduction
location
NC
NC
NC
NC
weather
hmd
weather
NC
NC
NC
NC
weather
NC
weather
NC
NC
NC
NC
location
crime
rule 3
rule 4
rule 5
actual
NC
eng-lon weather
crime
eng-lon
crime
eng-lon
crime
NC
eng-lon location
crime
NC
NC
NC
crime
deduction deduction deduction
NC
location location location
NC
NC
NC
NC
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather weather weather
NC
NC
weather weather
NC
NC
india
weather
NC
NC
NC
NC
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
NC
weather
hmd
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
NC
NC
weather
hmd
NC
90
Table B3
Sherlock Holmes – Cooper Beaches – Word Count Data
Paragraph weather
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
0
0
0
0
1
0
1
3
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
0
0
0
0
0
army
deduction
crime
india
location
eng-lon
hmd
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
1
0
2
0
2
1
2
0
3
0
0
0
0
0
0
1
1
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
2
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
2
0
0
1
1
1
0
0
0
0
0
0
0
0
1
0
1
0
0
2
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
91
Table B3 (continued).
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
0
0
0
4
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
3
0
0
0
0
0
1
0
0
2
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
0
1
0
0
4
0
0
0
1
3
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
0
1
0
0
0
0
0
92
Table B3 (continued).
Paragraph weather
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
2
0
0
0
0
0
0
0
0
0
1
1
0
0
0
1
1
0
3
0
1
0
0
0
0
0
0
0
4
0
0
1
army
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
deduction crime
5
2
0
0
0
0
2
1
1
0
1
1
1
0
0
0
1
0
4
2
0
0
0
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
india
1
0
0
0
1
0
0
0
1
0
0
1
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
3
1
0
1
location eng-lon
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
hmd
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
93
Table B3 (continued).
Paragraph weather
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
0
0
0
2
0
0
0
1
1
0
0
1
0
1
0
5
0
1
0
0
0
0
2
0
1
0
0
0
0
0
1
0
army
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
3
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
deduction crime
0
1
0
6
0
0
1
0
0
0
0
0
1
0
0
1
6
3
0
0
0
1
2
1
3
0
0
0
0
0
0
0
1
2
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
india
0
0
0
2
0
0
0
0
3
0
0
0
0
1
0
6
2
1
0
2
0
0
2
1
2
0
0
0
0
0
0
0
location eng-lon
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
1
0
0
0
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
hmd
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
4
0
0
1
1
0
0
0
0
0
0
0
0
0
0
94
Table B3 (continued).
Paragraph weather
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
0
0
0
0
0
2
0
0
0
0
0
0
0
0
1
1
2
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
army
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
deduction crime
0
0
0
0
0
0
3
0
1
0
0
0
0
1
3
1
0
1
0
0
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
1
1
1
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
india
0
0
0
0
1
3
0
0
1
3
0
0
0
0
2
0
2
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
location eng-lon
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
hmd
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
95
Table B3 (continued).
Paragraph weather
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
0
1
0
0
0
0
0
0
0
4
0
0
1
0
0
0
0
1
1
0
0
0
0
0
0
2
0
0
0
0
0
0
army
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
deduction crime
0
0
0
0
0
0
0
1
0
4
0
2
0
0
0
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
india
0
0
0
0
0
0
0
0
0
2
0
1
0
0
3
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
location eng-lon
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
hmd
0
1
0
0
0
0
0
0
0
2
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
96
Table B3 (continued).
Paragraph weather
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
1
0
0
0
1
1
0
0
1
2
1
0
0
0
0
0
0
0
1
1
army
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
deduction crime
1
0
2
0
0
0
0
1
0
4
1
0
1
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
india
1
0
3
0
0
0
0
0
0
3
0
0
0
0
1
0
0
0
1
3
location eng-lon
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
2
hmd
0
0
1
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
1
2
97
Table B4
Sherlock Holmes – Cooper Beaches – Rule Context Match
Paragraph
rule 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
NC
NC
deduction
NC
weather
NC
crime
hmd
weather
NC
hmd
india
india
NC
NC
NC
NC
eng-lon
deduction
weather
deduction
india
NC
army
hmd
NC
india
india
NC
NC
rule 2
rule 3
rule 4
NC
NC
NC
NC
NC
NC
deduction
NC
india
NC
NC
NC
deduction
NC
weather
NC
NC
NC
deduction
NC
crime
weather
NC
weather
weather
NC
weather
NC
NC
NC
deduction
NC
hmd
india
india
india
india
india
india
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
NC
eng-lon
deduction deduction deduction
weather weather weather
deduction deduction
NC
india
india
india
NC
NC
NC
army
NC
army
hmd
NC
weather
NC
NC
NC
india
india
india
india
india
india
NC
NC
NC
NC
NC
NC
rule 5
NC
NC
india
NC
deduction
NC
weather
deduction
deduction
NC
deduction
india
india
NC
NC
NC
NC
deduction
deduction
weather
NC
india
NC
weather
crime
NC
india
india
NC
NC
actual
NC
NC
deduction
NC
deduction
NC
crime
weather
deduction
NC
deduction
india
india
NC
NC
NC
NC
deduction
deduction
NC
deduction
india
NC
army
deduction
NC
deduction
deduction
NC
NC
98
Table B4 (continued).
Paragraph
rule 1
rule 2
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
NC
NC
india
weather
NC
india
NC
army
crime
india
india
NC
crime
NC
deduction
deduction
NC
NC
NC
NC
NC
hmd
hmd
india
deduction
NC
hmd
deduction
NC
NC
NC
weather
NC
NC
india
weather
NC
india
NC
army
crime
india
india
NC
crime
NC
deduction
deduction
NC
NC
NC
NC
NC
hmd
hmd
india
deduction
NC
weather
deduction
NC
NC
NC
weather
rule 3
rule 4
rule 5
NC
NC
NC
NC
NC
NC
NC
india
army
weather
NC
NC
NC
NC
NC
india
india
india
NC
NC
NC
army
army
army
NC
army
army
india
NC
NC
india
india
india
NC
NC
NC
NC
crime
weather
NC
NC
NC
deduction
NC
NC
deduction deduction deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
hmd
weather
hmd
NC
NC
india
india
india
deduction deduction deduction
NC
NC
NC
NC
hmd
weather
NC
army
army
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather weather
actual
NC
NC
army
NC
NC
deduction
NC
army
deduction
NC
india
NC
crime
NC
NC
deduction
NC
NC
NC
NC
NC
hmd
NC
deduction
deduction
NC
NC
deduction
NC
NC
NC
eng-lon
99
Table B4 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
deduction
deduction
NC
NC
india
NC
deduction
deduction
india
NC
weather
deduction
deduction
NC
NC
weather
hmd
NC
deduction
deduction
weather
NC
NC
NC
NC
NC
NC
deduction
hmd
eng-lon
NC
army
deduction
deduction
NC
NC
india
NC
deduction
deduction
india
NC
weather
deduction
deduction
NC
NC
weather
hmd
NC
deduction
deduction
weather
NC
NC
NC
NC
NC
NC
deduction
weather
eng-lon
NC
deduction
deduction
deduction
NC
NC
NC
NC
deduction
NC
NC
NC
NC
NC
deduction
NC
NC
weather
NC
NC
NC
deduction
weather
NC
NC
NC
NC
NC
NC
deduction
NC
NC
NC
NC
deduction
deduction
NC
NC
india
NC
deduction
deduction
india
NC
weather
deduction
NC
NC
NC
weather
weather
NC
deduction
deduction
weather
NC
NC
NC
NC
NC
NC
NC
hmd
eng-lon
NC
army
deduction
deduction
NC
NC
army
NC
deduction
crime
deduction
NC
weather
weather
NC
NC
NC
weather
weather
NC
weather
deduction
weather
NC
NC
NC
NC
NC
NC
NC
eng-lon
india
NC
crime
actual
NC
NC
deduction
NC
NC
NC
NC
india
NC
NC
deduction
deduction
NC
deduction
deduction
deduction
NC
NC
NC
hmd
NC
deduction
deduction
NC
NC
NC
NC
NC
NC
NC
deduction
india
100
Table B4 (continued).
Paragraph
rule 1
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
crime
crime
NC
deduction
NC
NC
deduction
hmd
weather
NC
NC
weather
deduction
india
NC
india
deduction
hmd
NC
india
hmd
hmd
army
india
weather
NC
NC
NC
NC
NC
weather
NC
rule 2
rule 3
rule 4
rule 5
crime
crime
crime
crime
crime
crime
crime
crime
NC
NC
NC
NC
deduction
NC
deduction weather
NC
NC
NC
NC
NC
NC
NC
NC
deduction deduction
NC
NC
hmd
NC
hmd
weather
india
NC
india
india
NC
NC
NC
NC
NC
NC
NC
NC
weather
NC
weather weather
deduction
NC
deduction
army
india
NC
hmd
weather
NC
NC
NC
NC
india
india
weather
army
deduction deduction deduction deduction
hmd
NC
hmd
army
NC
NC
NC
NC
india
india
india
india
hmd
hmd
hmd
hmd
hmd
NC
hmd
deduction
weather
NC
army
deduction
india
NC
india
deduction
deduction
NC
weather
india
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather weather weather weather
NC
NC
NC
NC
actual
india
NC
deduction
crime
crime
NC
deduction
NC
NC
NC
NC
india
NC
NC
army
army
india
NC
india
deduction
deduction
NC
india
hmd
hmd
india
india
deduction
NC
NC
india
NC
101
Table B4 (continued).
Paragraph
rule 1
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
NC
NC
NC
NC
army
weather
hmd
crime
deduction
india
NC
NC
NC
deduction
army
weather
army
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction
NC
NC
rule 2
rule 3
rule 4
rule 5
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
army
NC
hmd
hmd
india
NC
weather
india
deduction
NC
hmd
deduction
crime
crime
crime
crime
deduction
NC
deduction crime
india
NC
india
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction deduction deduction deduction
deduction
NC
army
weather
weather
NC
weather weather
weather
army
army
army
deduction deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
india
deduction deduction
army
NC
NC
NC
NC
NC
NC
NC
NC
actual
NC
NC
NC
NC
deduction
NC
NC
hmd
india
deduction
crime
deduction
india
NC
NC
NC
deduction
deduction
deduction
india
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
102
Table B4 (continued).
Paragraph
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
rule 1
rule 2
rule 3
rule 4
rule 5
NC
NC
NC
NC
NC
hmd
hmd
NC
hmd
weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction deduction deduction
NC
NC
NC
NC
NC
NC
NC
weather
weather
NC
weather deduction
NC
NC
NC
NC
NC
hmd
deduction
NC
hmd
crime
weather
weather
NC
weather
army
crime
crime
crime
NC
NC
india
india
india
india
india
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
hmd
hmd
NC
hmd
weather
army
army
NC
deduction deduction
deduction deduction deduction deduction deduction
NC
NC
NC
NC
NC
crime
crime
NC
crime
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
weather weather weather weather
india
india
india
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
actual
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction
NC
deduction
crime
crime
NC
NC
deduction
deduction
NC
deduction
NC
NC
NC
deduction
india
india
NC
NC
NC
NC
103
Table B4 (continued).
Paragraph
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
rule 1
rule 2
rule 3
rule 4
rule 5
deduction deduction
NC
deduction
army
NC
NC
NC
NC
NC
india
india
NC
india
crime
NC
NC
NC
NC
NC
weather
weather weather weather weather
weather
weather weather
NC
NC
NC
NC
NC
NC
NC
deduction deduction deduction
NC
NC
weather
weather weather weather weather
india
hmd
NC
india
deduction
weather
weather
NC
weather weather
NC
NC
NC
NC
NC
eng-lon
eng-lon
NC
eng-lon deduction
NC
NC
NC
NC
NC
army
army
NC
army
army
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
hmd
hmd
NC
hmd
weather
india
india
india
india
india
actual
deduction
NC
india
NC
NC
NC
NC
india
NC
deduction
deduction
NC
deduction
NC
army
NC
NC
NC
india
NC
104
Table B5
Sherlock Holmes – Engineers Thumb – Word Count Data
Paragraph weather army deduction crime india location eng-lon hmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
0
0
0
2
0
0
0
0
0
0
0
2
1
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
1
0
0
3
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
3
0
0
0
0
0
2
2
0
0
0
0
0
2
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
2
0
1
0
0
0
1
1
0
1
1
1
0
1
0
0
0
0
0
0
2
105
Table B5 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
0
0
0
2
0
2
0
0
0
0
0
2
0
0
0
2
0
2
0
0
0
0
1
0
1
0
0
0
0
1
1
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
1
2
1
1
1
0
1
0
0
0
0
0
0
0
1
2
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
2
0
0
1
0
1
0
0
1
0
1
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
1
0
0
0
3
0
1
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
2
0
1
0
1
0
0
0
0
0
1
0
3
0
2
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
106
Table B5 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
1
0
0
0
0
0
2
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
4
0
1
0
0
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
2
0
0
0
1
0
0
0
2
1
0
0
0
0
0
0
0
0
2
1
0
0
0
1
4
0
0
0
1
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
107
Table B5 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
0
0
1
2
2
2
0
2
0
0
0
0
1
0
0
0
0
0
0
0
4
1
2
4
0
0
0
1
5
2
1
0
0
0
0
5
3
1
0
1
0
1
0
2
1
1
0
1
0
0
0
0
1
1
3
1
0
0
0
1
2
0
0
0
0
0
1
1
2
0
3
1
0
0
0
2
0
1
1
1
0
0
0
0
1
0
0
2
0
0
0
0
2
0
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
0
0
0
0
3
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
6
2
0
6
1
0
0
1
2
0
0
0
0
0
1
1
1
2
0
2
2
0
0
1
0
4
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
2
1
0
0
108
Table B5 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
0
2
2
0
0
0
2
0
1
0
0
0
0
0
2
0
0
0
1
0
0
1
1
0
0
0
0
0
1
0
0
0
0
0
3
0
1
1
1
1
1
0
0
0
1
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
1
1
2
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
0
0
0
0
0
4
2
3
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
3
2
0
0
0
0
0
0
1
2
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
4
2
0
3
0
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
109
Table B5 (continued).
Paragraph weather army deduction crime india location eng-lon hmd
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
0
1
2
0
1
1
0
0
0
0
0
0
0
0
3
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
5
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
2
1
1
0
1
1
0
0
1
3
1
1
1
2
2
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
110
Table B6
Sherlock Holmes – Engineers Thumb – Rule Context Match
Paragraph
rule 1
rule 2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
NC
NC
army
weather
eng-lon
NC
NC
hmd
deduction
hmd
eng-lon
weather
weather
crime
NC
hmd
hmd
location
hmd
hmd
hmd
location
hmd
NC
NC
NC
army
NC
NC
hmd
NC
NC
location
location
eng-lon
NC
NC
hmd
deduction
hmd
location
weather
weather
crime
NC
weather
location
location
hmd
hmd
hmd
location
hmd
NC
NC
NC
army
NC
NC
hmd
rule 3
rule 4
NC
NC
NC
NC
NC
deduction
NC
weather
NC
eng-lon
NC
NC
NC
NC
hmd
deduction
NC
deduction
NC
hmd
NC
eng-lon
NC
weather
weather weather
crime
NC
NC
NC
NC
hmd
NC
location
location location
NC
hmd
hmd
NC
hmd
hmd
location location
NC
hmd
NC
NC
NC
NC
NC
NC
army
NC
NC
NC
NC
NC
NC
army
rule 5
actual
NC
NC
crime
hmd
army
NC
NC
hmd
crime
location
crime
weather
weather
NC
NC
weather
location
location
army
NC
hmd
location
army
NC
NC
NC
NC
NC
NC
weather
NC
NC
crime
hmd
NC
NC
NC
hmd
crime
hmd
location
NC
weather
crime
NC
hmd
location
NC
hmd
hmd
hmd
NC
NC
NC
crime
NC
crime
crime
crime
hmd
111
Table B6 (continued).
Paragraph
rule 1
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
NC
hmd
NC
crime
NC
weather
NC
NC
NC
NC
eng-lon
eng-lon
location
hmd
NC
eng-lon
crime
deduction
deduction
deduction
location
NC
location
NC
eng-lon
NC
NC
location
NC
weather
weather
deduction
rule 2
rule 3
rule 4
rule 5
NC
NC
NC
NC
hmd
NC
hmd
eng-lon
NC
NC
NC
NC
army
NC
crime
weather
NC
NC
NC
NC
weather weather weather weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
NC
location location
weather
NC
eng-lon weather
location location location location
hmd
hmd
hmd
hmd
NC
NC
NC
NC
eng-lon
NC
eng-lon location
crime
NC
crime deduction
deduction
NC
deduction weather
deduction deduction deduction deduction
deduction deduction deduction deduction
location
NC
location eng-lon
NC
NC
NC
NC
location
NC
location
army
NC
NC
NC
NC
eng-lon
NC
weather weather
NC
NC
NC
NC
NC
NC
NC
NC
location location
NC
NC
NC
NC
NC
NC
weather weather weather weather
weather
NC
deduction
army
deduction deduction deduction deduction
actual
NC
hmd
NC
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
hmd
NC
eng-lon
deduction
deduction
deduction
deduction
deduction
NC
NC
NC
crime
NC
NC
NC
NC
NC
NC
deduction
112
Table B6 (continued).
Paragraph
rule 1
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
location
NC
NC
deduction
NC
NC
NC
location
NC
NC
NC
location
weather
NC
NC
deduction
deduction
NC
NC
eng-lon
NC
eng-lon
location
eng-lon
NC
hmd
hmd
eng-lon
NC
NC
NC
location
rule 2
rule 3
rule 4
rule 5
actual
location
NC
location deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
deduction
army deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
eng-lon
NC
eng-lon
eng-lon location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location location location location location
weather
NC
weather weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction
NC
deduction weather deduction
deduction deduction deduction deduction deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
eng-lon
eng-lon
eng-lon
NC
NC
NC
NC
NC
NC
deduction
NC
eng-lon weather deduction
location location location location location
eng-lon
NC
eng-lon deduction deduction
NC
NC
NC
NC
NC
hmd
NC
hmd
weather
crime
deduction
NC
crime
eng-lon deduction
location
NC
eng-lon location location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
location
hmd
NC
113
Table B6 (continued).
Paragraph
rule 1
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
NC
NC
deduction
army
location
weather
crime
location
NC
army
location
army
army
army
deduction
hmd
NC
location
location
location
army
weather
weather
weather
NC
NC
location
hmd
location
weather
eng-lon
NC
rule 2
rule 3
rule 4
rule 5
actual
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction
NC
deduction weather
NC
location
NC
location weather location
army
NC
location weather
army
weather
NC
NC
NC
location
location
NC
deduction deduction deduction
weather
NC
location
army
NC
NC
NC
NC
NC
NC
army
army
NC
NC
NC
location location
NC
NC
NC
deduction
NC
army
location
NC
army
NC
army
army
army
army
NC
NC
NC
deduction
deduction deduction deduction deduction
NC
hmd
NC
hmd
army
NC
NC
NC
NC
NC
NC
location location
NC
NC
NC
location location location location
NC
location
NC
location
crime
NC
weather
NC
army
weather weather
weather
NC
NC
NC
location
army
NC
army
location
NC
weather
NC
weather weather deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location location
NC
NC
NC
hmd
NC
hmd
weather
NC
weather
NC
location weather location
crime
weather
crime
weather
NC
eng-lon
NC
weather weather
NC
NC
NC
NC
NC
NC
114
Table B6 (continued).
Paragraph
rule 1
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
NC
weather
location
NC
crime
crime
crime
location
hmd
NC
NC
NC
army
hmd
weather
eng-lon
location
NC
weather
NC
NC
weather
weather
crime
deduction
hmd
NC
NC
deduction
crime
NC
location
rule 2
rule 3
rule 4
rule 5
actual
NC
NC
NC
NC
NC
weather weather
NC
NC
location
army
NC
deduction
army
army
NC
NC
NC
NC
NC
crime
crime
crime
crime
crime
hmd
NC
crime
army
crime
location
crime
crime
army
crime
location location location
army
NC
hmd
NC
hmd
deduction crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
army
NC
army
army
NC
hmd
hmd
hmd
hmd
NC
army
weather weather weather
NC
location
NC
location
army
location
location location location location location
NC
NC
NC
NC
NC
weather weather weather weather
hmd
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather
NC
NC
NC
NC
weather
NC
weather deduction location
crime
crime
crime
crime
NC
deduction deduction deduction deduction
NC
hmd
hmd
hmd
hmd
location
NC
NC
NC
NC
location
NC
NC
NC
NC
location
deduction
NC
deduction weather location
crime
crime
NC
NC
NC
NC
NC
NC
NC
NC
location location location location location
115
Table B6 (continued).
Paragraph
rule 1
rule 2
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
NC
weather
weather
location
eng-lon
location
location
location
crime
location
location
NC
NC
location
location
location
location
location
weather
location
eng-lon
army
NC
weather
weather
location
eng-lon
location
location
location
crime
location
location
NC
NC
location
army
location
location
location
weather
location
eng-lon
army
rule 3
rule 4
NC
NC
weather weather
weather
NC
location location
NC
eng-lon
location weather
NC
location
location
NC
crime
crime
location location
location location
NC
NC
NC
NC
NC
location
NC
location
NC
crime
NC
location
NC
deduction
NC
weather
NC
location
eng-lon
eng-lon
army
army
rule 5
actual
NC
NC
weather
NC
NC
NC
location location
weather
NC
weather
crime
army
NC
NC
NC
crime
crime
location location
location location
NC
NC
NC
NC
crime
NC
weather location
crime
location
army
NC
eng-lon deduction
weather
NC
crime
crime
eng-lon
NC
army
NC
116
Table B7
Sherlock Holmes – Nobel Bachelor – Word Count Data
Paragraph weather army deduction crime
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
0
0
0
7
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
2
0
0
1
0
0
0
0
2
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
5
0
0
1
2
0
0
0
0
0
0
4
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
1
0
0
2
1
0
2
0
0
1
1
0
1
0
2
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
0
0
3
0
0
0
0
0
0
0
0
117
Table B7 (continued).
Paragraph weather army deduction crime
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
5
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
1
2
2
0
1
0
0
0
0
0
0
0
2
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
1
1
1
2
0
0
0
0
0
0
0
0
1
0
0
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
1
7
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
0
0
0
118
Table B7 (continued).
Paragraph weather army deduction crime
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
119
Table B7 (continued).
Paragraph weather army deduction crime
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
1
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
1
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
120
Table B7 (continued).
Paragraph weather army deduction crime
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
1
1
0
0
1
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
2
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
1
1
0
0
0
0
0
1
0
0
0
0
0
0
1
121
Table B7 (continued).
Paragraph weather army deduction crime
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
0
0
0
2
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
2
0
0
0
0
2
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
1
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
122
Table B7 (continued).
Paragraph weather army deduction crime
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
4
0
4
2
0
3
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
2
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
2
1
1
1
2
0
0
0
1
1
2
2
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
3
0
0
0
0
1
1
1
2
1
1
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
2
0
1
0
1
0
0
0
0
0
123
Table B7 (continued).
Paragraph weather army deduction crime
222
223
224
225
226
227
228
229
230
231
232
0
0
0
0
3
0
1
0
0
0
1
0
1
0
0
1
0
0
0
1
0
0
0
1
3
0
1
0
0
0
2
1
1
0
0
1
1
0
0
0
0
0
0
0
india location eng-lon hmd
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
3
0
0
0
3
0
1
1
0
0
0
1
0
1
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
Table B8
Sherlock Holmes – Nobel Bachelor – Rule Context Match
Paragraph
1
2
3
4
5
6
7
8
9
rule 1
rule 2
NC
NC
NC
NC
deduction deduction
weather weather
weather weather
NC
NC
NC
NC
NC
NC
NC
NC
rule 3
rule 4
rule 3
actual
NC
NC
NC
NC
weather
NC
NC
NC
NC
NC
NC
army
location
weather
NC
NC
NC
NC
NC
NC
army
crime
weather
NC
NC
NC
NC
NC
NC
crime
weather
NC
NC
NC
NC
NC
124
Table B8 (continued).
Paragraph
rule 1
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
NC
NC
eng-lon
NC
location
deduction
location
NC
hmd
location
crime
NC
weather
deduction
eng-lon
location
location
NC
location
NC
deduction
NC
location
NC
NC
NC
NC
NC
NC
NC
NC
rule 2
rule 3
rule 4
rule 3
actual
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
eng-lon
eng-lon
NC
NC
eng-lon
NC
NC
NC
NC
NC
location
NC
location
eng-lon
crime
deduction deduction deduction deduction deduction
location
NC
location
army
crime
NC
NC
NC
NC
deduction
hmd
NC
weather weather deduction
deduction
NC
deduction deduction
NC
crime
NC
crime
location
NC
NC
NC
NC
NC
NC
hmd
NC
weather weather
NC
deduction
NC
deduction
crime
crime
eng-lon
eng-lon
eng-lon
eng-lon deduction
location
NC
location
army
NC
location
location
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
location
army
NC
NC
NC
NC
NC
NC
deduction
NC
location
weather deduction
NC
NC
NC
NC
location
location
NC
army
eng-lon
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
crime
NC
NC
NC
NC
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction
125
Table B8 (continued).
Paragraph
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
rule 1
rule 2
NC
NC
location
location
hmd
deduction
NC
NC
NC
NC
NC
NC
weather weather
crime
deduction
deduction weather
crime
crime
hmd
hmd
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather deduction
NC
NC
NC
NC
eng-lon
eng-lon
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
location
NC
NC
NC
NC
rule 3
NC
location
NC
NC
NC
NC
NC
NC
NC
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
rule 4
rule 3
actual
NC
NC
NC
location
location
NC
hmd
weather
NC
NC
NC
deduction
NC
NC
NC
NC
NC
NC
weather location
NC
crime
weather
crime
deduction weather
NC
army
army
weather
hmd
deduction
NC
NC
NC
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather deduction
NC
NC
NC
NC
NC
NC
crime
eng-lon deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
location
NC
NC
NC
location
location
NC
NC
NC
NC
NC
NC
NC
126
Table B8 (continued).
Paragraph
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
rule 1
rule 2
rule 3
rule 4
rule 3
actual
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather weather
NC
weather weather location
deduction deduction deduction deduction deduction
NC
army
army
NC
army
deduction
NC
NC
NC
NC
NC
NC
deduction
weather weather weather weather weather deduction
eng-lon
eng-lon
eng-lon
NC
NC
NC
eng-lon
eng-lon
eng-lon
eng-lon
eng-lon
NC
NC
NC
NC
NC
NC
NC
weather weather weather weather weather
NC
weather weather weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction deduction deduction location deduction
NC
NC
NC
NC
NC
NC
NC
army
army
NC
army
deduction deduction
army
army
army
army
army
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
location
location
NC
NC
NC
location
location
location
location
location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
127
Table B8 (continued).
Paragraph
rule 1
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
weather
NC
weather
NC
NC
NC
NC
NC
weather
NC
eng-lon
weather
location
NC
NC
NC
deduction
deduction
NC
weather
crime
location
weather
weather
NC
deduction
location
NC
crime
NC
NC
NC
rule 2
rule 3
rule 4
rule 3
weather weather weather weather
NC
NC
NC
NC
weather weather
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather weather
NC
NC
NC
NC
NC
NC
eng-lon
NC
army
army
weather weather weather weather
deduction
NC
location
army
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
deduction deduction deduction deduction
deduction deduction deduction deduction
NC
NC
NC
NC
weather weather weather weather
crime
crime
NC
NC
location
location
location
location
weather weather weather weather
weather weather
NC
NC
NC
NC
NC
NC
deduction deduction deduction deduction
location
location
location
location
NC
NC
NC
NC
crime
crime
crime
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
actual
NC
deduction
deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
deduction
NC
NC
NC
NC
NC
NC
NC
crime
NC
deduction
NC
NC
location
location
NC
crime
NC
128
Table B8 (continued).
Paragraph
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
rule 1
rule 2
rule 3
rule 4
NC
NC
NC
NC
NC
NC
NC
NC
army
army
army
army
hmd
hmd
NC
hmd
NC
NC
NC
NC
deduction deduction deduction deduction
NC
NC
NC
NC
hmd
weather
NC
hmd
weather weather
NC
NC
eng-lon
eng-lon
NC
eng-lon
crime
crime
crime
deduction
NC
NC
NC
NC
NC
NC
NC
NC
weather weather weather weather
hmd
hmd
hmd
NC
NC
NC
NC
NC
deduction deduction deduction deduction
weather weather weather weather
weather weather
NC
NC
NC
NC
NC
NC
weather weather
NC
weather
hmd
hmd
NC
hmd
NC
NC
NC
NC
hmd
hmd
NC
location
NC
NC
NC
NC
weather weather weather weather
weather weather
NC
NC
location
location
location
location
deduction deduction deduction deduction
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
rule 3
actual
NC
NC
NC
NC
army
NC
deduction
NC
NC
NC
deduction
NC
NC
NC
weather
NC
NC
NC
weather
NC
weather
NC
NC
deduction
NC
deduction
weather
NC
NC
NC
NC
NC
deduction
hmd
weather
NC
NC
NC
NC
NC
weather
NC
army
NC
NC
NC
location
NC
NC
NC
weather
hmd
NC
NC
location
NC
deduction
NC
NC
NC
NC
NC
NC
NC
129
Table B8 (continued).
Paragraph
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
rule 1
rule 2
rule 3
rule 4
NC
NC
NC
NC
crime
crime
NC
crime
NC
NC
NC
NC
location
location
location
NC
deduction deduction
NC
deduction
NC
NC
NC
NC
NC
NC
NC
NC
crime
crime
NC
location
location
location
location
location
location
location
location
location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
weather weather weather weather
crime
crime
NC
army
NC
NC
NC
NC
NC
NC
NC
NC
army
army
army
NC
location
location
NC
hmd
location
location
NC
location
NC
NC
NC
NC
army
army
army
NC
deduction deduction deduction
army
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
crime
crime
crime
crime
rule 3
actual
NC
army
NC
NC
army
NC
NC
location
location
location
NC
NC
NC
weather
army
NC
NC
NC
weather
weather
NC
NC
army
NC
NC
NC
NC
NC
NC
NC
NC
crime
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
location
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
130
Table B8 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
crime
deduction
deduction
location
location
deduction
NC
NC
army
deduction
army
army
weather
weather
location
crime
location
NC
NC
NC
NC
eng-lon
deduction
crime
crime
weather
NC
weather
NC
eng-lon
deduction
weather
crime
deduction
deduction
location
location
deduction
NC
NC
army
deduction
weather
deduction
weather
weather
location
crime
location
NC
NC
NC
NC
eng-lon
deduction
location
crime
location
NC
weather
NC
location
deduction
weather
crime
deduction
deduction
NC
NC
deduction
NC
NC
army
NC
NC
NC
weather
NC
location
NC
NC
NC
NC
NC
NC
eng-lon
NC
NC
crime
weather
NC
NC
NC
NC
deduction
NC
crime
deduction
deduction
location
location
deduction
NC
NC
army
location
army
deduction
weather
hmd
location
crime
location
NC
NC
NC
NC
eng-lon
deduction
deduction
crime
weather
NC
NC
NC
eng-lon
deduction
weather
rule 3
actual
crime
NC
deduction
NC
deduction
NC
deduction
NC
weather
NC
deduction
NC
NC
location
NC
NC
army
NC
location
NC
weather
NC
location
NC
weather location
eng-lon
location
location
NC
deduction
NC
deduction
NC
NC
crime
NC
NC
NC
NC
NC
NC
eng-lon
NC
army
NC
deduction
NC
crime
deduction
weather location
NC
NC
NC
NC
NC
NC
army
NC
deduction
NC
deduction
NC
131
Table B9
The Storm – Chapter 5 - Word Count Data
Paragraph weather social political geography engineering
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
0
0
9
17
8
1
5
6
3
5
11
5
2
23
2
22
8
3
1
0
1
0
0
0
0
3
5
0
0
2
6
0
3
1
1
4
0
3
1
2
4
3
2
4
2
3
0
0
0
0
0
0
3
4
0
0
12
3
6
1
0
6
9
4
7
2
8
2
0
3
4
2
1
0
0
0
0
0
0
6
1
0
0
6
1
2
0
1
3
2
1
9
1
1
6
1
9
2
4
2
0
0
0
0
0
0
0
3
0
0
5
12
4
2
3
4
6
3
9
2
4
15
2
8
3
1
2
0
1
0
0
1
0
1
5
132
Table B10
The Storm – Chapter 5 – Rule Context Match
Paragraph
rule 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
NC
NC
political
social
political
engineering
weather
weather
engineering
weather
political
weather
political
political
weather
geography
political
social
social
NC
engineering
NC
NC
engineering
NC
political
engineering
rule 2
rule 3
rule 4
rule 5
actual
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
political
political
political
political
political
weather
NC
social
weather
weather
weather
NC
geography
weather
social
social
NC
engineering political
social
weather
weather
weather
weather
social
weather
NC
political
social
social
political
NC
engineering
social
political
weather
NC
political
weather
weather
weather
NC
political
weather
political
weather
weather
weather
weather
social
political
NC
political engineering political
weather
NC
political
weather
weather
social
NC
social
social
social
weather
NC
weather
weather
weather
weather
NC
political
weather
social
geography
NC
geography
weather geography
social
NC
social
weather
social
NC
NC
NC
NC
NC
engineering
NC
weather
weather engineering
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
engineering engineering
NC
NC
engineering
NC
NC
NC
NC
NC
political
NC
political
social
political
engineering
NC
engineering weather
social
133
Table B11
The Storm – Chapter 6 - Word Count Data
Paragraph
weather
social
political
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
0
0
8
4
3
3
7
11
8
1
0
1
5
2
3
2
3
2
0
3
16
4
2
2
2
4
6
0
1
0
0
0
3
1
1
10
7
0
4
4
2
6
1
5
2
2
2
4
0
1
3
8
1
3
1
2
5
4
1
1
0
0
0
1
2
12
3
4
3
3
1
14
1
4
1
0
9
9
6
5
5
6
6
1
2
1
8
11
4
3
geography engineering
0
0
5
5
3
8
1
8
5
1
0
0
1
2
3
1
1
3
3
0
12
3
0
1
1
0
1
3
1
1
0
1
1
1
6
3
1
5
5
0
1
0
2
3
1
2
0
1
1
3
13
12
2
6
7
12
14
4
1
3
134
Table B11 (continued).
Paragraph
weather
social
political
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
4
1
3
1
4
1
5
0
1
4
4
0
4
1
1
0
0
0
0
3
3
1
0
0
0
0
0
5
6
11
2
2
2
1
3
1
2
2
1
2
3
4
3
1
2
2
1
0
5
5
2
5
5
0
1
4
2
1
0
3
3
2
1
2
6
4
8
1
1
0
1
6
5
9
2
4
4
6
5
0
5
3
0
5
7
3
1
0
0
1
0
8
2
1
2
1
geography engineering
2
4
1
0
0
0
0
2
3
6
0
0
1
1
0
0
4
4
2
1
4
1
0
1
0
0
0
4
4
4
0
1
9
5
10
3
5
3
7
0
2
7
6
1
0
1
1
0
3
1
2
0
6
0
1
1
0
0
0
3
1
4
0
1
135
Table B11 (continued).
Paragraph
weather
social
political
63
64
65
66
67
8
0
3
3
0
3
0
7
2
0
11
1
10
4
1
geography engineering
2
0
2
0
0
7
0
6
3
1
rule 4
rule 5
Table B12
The Storm – Chapter 6 – Rule Context Match
Paragraph
rule 1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
NC
engineering
weather
weather
engineering
weather
political
engineering
weather
social
social
social
geography
engineering
geography
rule 2
rule 3
actual
NC
NC
NC
NC
NC
engineering engineering
NC
NC
NC
weather
weather
weather
weather
weather
geography
NC
weather geography geography
engineering
NC
engineering weather engineering
political
NC
political
social
social
social
NC
social
social
social
weather
NC
weather
weather
weather
weather
NC
weather
social
political
social
NC
social
political
social
social
social
social
social
social
political
NC
social
political
political
weather
NC
geography weather
social
social
NC
engineering
social
political
geography
NC
geography weather
political
136
Table B12 (continued).
Paragraph
rule 1
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
weather
political
geography
political
engineering
engineering
geography
social
engineering
engineering
political
political
political
political
engineering
political
political
engineering
political
engineering
engineering
engineering
geography
political
geography
engineering
social
political
political
weather
NC
engineering
rule 2
rule 3
rule 4
rule 5
actual
engineering
NC
engineering
social
social
political
NC
political
social
political
political
NC
social
political
political
political
political
political
political
political
political
NC
engineering
social
political
weather
NC
weather
social
weather
engineering
NC
geography
social
engineering
political
NC
engineering political
political
engineering engineering engineering engineering engineering
engineering
NC
engineering geography engineering
engineering
NC
political engineering engineering
engineering
NC
political
weather engineering
political
political
political
political
political
political
NC
political
weather
political
engineering
NC
engineering political
political
engineering
NC
political
weather engineering
engineering
NC
engineering engineering engineering
engineering
NC
engineering political engineering
engineering
NC
political
social
engineering
engineering
NC
engineering weather
social
engineering
NC
social
weather
social
engineering
NC
engineering
social
social
political
NC
social
political
social
political
NC
political
social
political
political
NC
geography geography political
engineering
NC
engineering weather
social
political
NC
engineering political
political
political
NC
political
weather
political
political
NC
political
social
social
political
NC
weather
political
social
NC
NC
NC
NC
NC
political
NC
engineering
social
social
137
Table B12 (continued).
Paragraph
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
rule 1
rule 2
geography
social
geography geography
weather
political
social
political
political
political
political
political
social
social
social
social
political
political
NC
NC
political
political
social
weather
weather
weather
weather
political
political
weather
engineering political
political
political
political
political
political
political
political
political
rule 3
NC
NC
NC
NC
political
NC
social
social
NC
NC
NC
NC
weather
weather
NC
NC
political
political
political
NC
rule 4
rule 5
actual
geography
social
social
social
social
social
political
social
political
engineering weather
political
political
political
political
political
social
political
social
social
social
social
social
social
political
social
political
NC
NC
NC
geography weather
political
social
weather engineering
weather
weather
social
political
social
social
geography
social
social
engineering weather
political
political
political
political
political
social
political
political
political
political
political engineering
social
138
Table B13
The Sorcerer’s Stone – Chapter 13 – Word Count Data
Paragraph magic darkmagic wands school clothes muggles
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2
1
0
0
0
0
1
0
0
0
0
0
1
1
2
0
0
1
0
1
0
0
0
2
0
1
0
0
0
0
1
harry
7
5
1
1
2
3
0
1
1
1
0
1
3
7
2
2
1
1
1
1
2
3
4
0
2
0
0
1
1
1
139
Table B13 (continued).
Paragraph magic darkmagic wands school clothes muggles
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
0
0
1
0
0
1
2
1
7
1
3
1
0
harry
4
0
1
1
0
2
1
0
2
0
2
0
0
2
1
0
2
0
4
2
7
2
1
8
3
1
12
5
8
3
1
140
Table B13 (continued).
Paragraph magic darkmagic wands school clothes muggles
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
0
0
0
1
1
2
0
0
0
0
2
2
0
0
0
0
0
1
0
1
0
0
2
1
0
1
1
0
0
harry
1
4
2
0
1
0
2
2
2
2
1
0
1
6
5
1
1
1
1
1
2
0
2
2
2
6
5
1
3
2
2
1
141
Table B13 (continued).
Paragraph magic darkmagic wands school clothes muggles
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
1
2
1
1
0
1
1
0
1
0
0
1
0
1
0
2
1
0
1
0
0
0
1
2
harry
0
4
0
1
1
8
1
3
1
1
1
1
2
0
1
2
0
3
0
0
1
4
1
5
1
0
6
3
142
Table B14
The Sorcerer’s Stone – Chapter 13 – Rule Context Match
Paragraph
rule 1
rule 2
rule 3
rule 4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
school
NC
NC
harry
harry
clothes
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
NC
NC
harry
harry
clothes
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
NC
NC
harry
harry
harry
harry
NC
harry
NC
harry
harry
harry
harry
NC
NC
NC
NC
NC
harry
NC
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
school
NC
NC
harry
harry
clothes
rule 5
actual
muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
darkmagic
harry
harry
harry
harry
harry
harry
NC
darkmagic
muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
muggles
harry
harry
harry
muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
muggles
harry
NC
darkmagic
NC
harry
clothes
harry
harry
harry
muggles
harry
143
Table B14 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
harry
NC
harry
harry
NC
harry
harry
school
harry
NC
harry
NC
NC
harry
harry
NC
harry
NC
harry
harry
harry
harry
magic
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
NC
harry
harry
school
harry
NC
harry
NC
NC
harry
harry
NC
harry
NC
harry
harry
harry
harry
magic
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
NC
NC
NC
school
NC
NC
NC
NC
NC
NC
harry
NC
harry
NC
harry
harry
harry
harry
NC
NC
NC
NC
NC
harry
harry
NC
harry
harry
harry
NC
harry
harry
NC
harry
harry
NC
harry
NC
harry
NC
NC
harry
harry
NC
harry
NC
harry
harry
harry
harry
magic
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
NC
muggles
magic
NC
magic
NC
muggles
NC
NC
muggles
harry
NC
harry
NC
harry
harry
harry
harry
magic
muggles
muggles
muggles
muggles
harry
harry
clothes
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
magic
harry
NC
NC
harry
harry
NC
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
144
Table B14 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
harry
harry
NC
harry
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
NC
harry
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
NC
harry
NC
harry
harry
NC
harry
harry
NC
harry
harry
NC
harry
harry
harry
harry
harry
NC
NC
harry
harry
NC
harry
harry
harry
NC
harry
harry
harry
NC
harry
harry
NC
harry
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
rule 5
actual
harry
harry
harry
harry
NC
harry
harry
harry
NC
harry
harry darkmagic
harry darkmagic
muggles
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
muggles
magic
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
muggles
harry
NC
NC
harry
harry
harry
harry
clothes
harry
harry
harry
harry
harry
harry
harry
clothes
harry
harry
harry
harry
harry
harry
harry
NC
harry
145
Table B14 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
harry
school
harry
harry
clothes
harry
harry
harry
muggles
harry
harry
harry
NC
harry
harry
NC
harry
NC
muggles
harry
harry
harry
harry
harry
NC
harry
harry
harry
school
harry
harry
harry
harry
harry
harry
muggles
harry
harry
harry
NC
harry
harry
NC
harry
NC
muggles
harry
harry
harry
harry
harry
NC
harry
harry
harry
school
NC
NC
NC
NC
harry
harry
NC
NC
harry
NC
NC
harry
NC
NC
NC
NC
muggles
NC
harry
NC
harry
harry
NC
NC
NC
harry
school
harry
harry
clothes
harry
harry
harry
muggles
harry
harry
harry
NC
harry
harry
NC
harry
NC
muggles
harry
harry
harry
harry
harry
NC
harry
harry
harry
school
muggles
muggles
harry
clothes
harry
harry
harry
muggles
harry
muggles
NC
harry
muggles
NC
muggles
NC
muggles
muggles
harry
muggles
harry
harry
NC
muggles
muggles
harry
darkmagic
harry
harry
darkmagic
harry
harry
darkmagic
harry
harry
harry
harry
NC
NC
harry
NC
darkmagic
harry
harry
harry
harry
NC
harry
harry
harry
NC
NC
146
Table B15
Order of the Phoenix – Chapter 32 – Word Count Data
Paragraph magic darkmagic wands school clothes muggles harry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
1
0
0
1
0
1
0
0
1
0
0
1
0
1
0
0
0
1
1
1
1
0
1
1
1
2
1
1
0
3
3
1
0
1
0
2
1
1
2
1
0
0
0
1
147
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
0
1
1
1
1
0
1
0
0
0
1
1
0
0
0
0
0
0
1
1
1
3
1
0
1
2
2
1
0
3
1
5
3
2
2
2
3
2
2
2
4
3
2
3
0
0
1
0
1
1
2
2
1
148
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
1
3
0
3
0
0
2
1
0
0
0
0
0
0
0
1
0
0
2
0
0
0
1
0
0
1
0
0
1
3
3
1
1
7
1
4
0
1
0
5
2
1
0
0
1
1
2
4
2
0
3
2
1
1
5
3
0
2
1
1
149
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
1
0
0
0
1
0
0
1
2
0
0
0
0
1
0
0
0
1
0
1
0
1
1
1
0
1
0
1
0
1
1
0
1
1
1
0
1
0
1
0
0
0
0
0
1
1
0
1
1
2
1
2
0
6
5
1
1
3
0
2
1
1
3
3
5
1
6
0
7
2
4
3
2
1
0
1
2
4
1
5
1
150
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
1
4
0
0
2
0
0
0
4
3
3
1
0
3
5
3
2
2
1
2
0
0
3
3
2
1
2
2
1
5
4
2
151
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
0
0
1
0
0
0
2
0
1
1
3
1
3
5
1
0
0
2
5
6
3
1
2
2
0
0
1
3
3
3
3
2
1
2
4
1
0
1
4
3
2
4
152
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
2
0
0
0
0
1
1
0
1
0
0
2
1
1
1
1
0
1
0
1
0
1
1
1
0
1
2
4
2
0
3
5
2
5
2
4
0
3
2
1
1
1
1
3
1
3
2
4
3
5
3
2
1
1
2
1
3
1
153
Table B15 (continued).
Paragraph magic darkmagic wands school clothes muggles harry
223
224
225
226
227
228
229
230
231
232
233
234
235
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
1
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
0
3
0
1
0
0
0
1
2
2
0
0
1
1
1
2
2
2
6
1
4
Table B16
Order of the Phoenix – Chapter 32 – Rule Context Match
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
1
2
3
4
5
6
7
NC
NC
NC
magic
harry
harry
harry
NC
NC
NC
magic
harry
harry
harry
NC
NC
NC
NC
harry
NC
harry
NC
NC
NC
harry
harry
harry
harry
NC
NC
NC
harry
harry
muggles
harry
NC
NC
NC
school
harry
magic
harry
154
Table B16 (continued).
Paragraph
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
rule 1
rule 2
rule 3
rule 4
rule 5
actual
NC
NC
NC
NC
NC
magic
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
clothes
NC
harry
harry
harry
harry
harry
harry
school
harry
NC
school
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
NC
harry
harry
NC
harry muggles
harry
clothes clothes clothes clothes clothes
NC
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry darkmagic
NC
NC
NC
NC
NC
NC
muggles muggles muggles muggles muggles
NC
NC
NC
NC
NC
NC
NC
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
NC
harry
harry
NC
harry
magic
harry
155
Table B16 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
NC
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
harry
harry
NC
NC
NC
harry
harry
harry
harry
NC
harry
harry
NC
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
rule 5
actual
NC
harry
harry
harry
harry
magic
muggles
harry
harry
harry
muggles
harry
harry
harry
harry
harry
harry darkmagic
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
harry
harry
NC
NC
harry
NC
harry
harry
magic
harry
muggles
harry
muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
muggles
harry
harry
harry
harry
harry
muggles
harry
NC
NC
156
Table B16 (continued).
Paragraph
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
rule 1
rule 2
rule 3
rule 4
rule 5
actual
harry
harry
harry
harry
harry
harry
muggles muggles muggles muggles muggles
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
harry
NC
NC
NC
NC
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
NC
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
magic
magic
NC
magic
magic darkmagic
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
NC
NC
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry muggles
harry
NC
NC
NC
NC
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
157
Table B16 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
NC
harry
harry
harry
harry
harry
muggles
harry
harry
NC
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
NC
NC
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
NC
NC
harry
NC
harry
harry
NC
harry
harry
NC
NC
harry
NC
NC
harry
harry
harry
NC
harry
NC
harry
harry
NC
harry
harry
NC
harry
NC
NC
NC
harry
NC
NC
NC
harry
NC
harry
harry
harry
harry
harry
muggles
harry
harry
NC
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
NC
NC
harry
rule 5
actual
NC
NC
harry
harry
harry
harry
muggles
harry
harry
harry
harry
magic
harry
harry
muggles
harry
harry
harry
NC
NC
clothes
harry
harry
harry
harry
harry
harry darkmagic
clothes
magic
harry
harry
NC
NC
harry
harry
harry
harry
muggles
harry
harry
harry
harry
harry
muggles
harry
harry
NC
muggles
harry
NC
NC
NC
NC
harry
harry
NC
NC
NC
NC
NC
harry
harry
harry
158
Table B16 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
NC
NC
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
NC
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
wands
wands
harry
harry
harry
magic
wands
harry
harry
harry
harry
harry
harry
harry
NC
NC
muggles
muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
wands
harry
NC
harry
harry
harry
harry
harry
harry
school
harry
harry
harry
harry
NC
NC
harry
harry
159
Table B16 (continued).
Paragraph
rule 1
rule 2
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
harry
harry
harry
school
harry
NC
NC
harry
harry
harry
harry
magic
harry
harry
harry
school
harry
magic
harry
school
harry
harry
harry
harry
harry
harry
NC
harry
harry
muggles
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
magic
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
rule 3
rule 4
rule 5
actual
harry
harry
harry
harry
NC
harry muggles
harry
NC
harry muggles
harry
NC
school
harry
harry
harry
harry
harry
magic
NC
NC
NC
NC
NC
NC
NC
NC
NC
harry muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
harry muggles
harry
NC
magic
magic
harry
harry muggles magic
harry
harry
harry
harry
harry
harry
harry
harry darkmagic
NC
school
harry
harry
harry
harry
harry
harry
magic
magic
magic
NC
harry
NC
NC
harry
NC
school muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
school
NC
harry
wands
harry
NC
harry muggles
harry
NC
NC
NC
harry
harry
harry
magic
harry
NC
harry
wands
harry
muggles muggles muggles
harry
harry
harry
harry
harry
harry
harry
harry
harry
160
Table B16 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
school
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
school
muggles
harry
harry
harry
NC
harry
NC
harry
NC
harry
harry
NC
NC
harry
harry
NC
harry
harry
harry
harry
harry
NC
NC
NC
harry
NC
NC
harry
NC
NC
NC
NC
NC
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
harry
school
harry
harry
harry
harry
NC
harry
muggles
harry
clothes
harry
harry
muggles
muggles
harry
harry
muggles
harry
harry
harry
harry
harry
muggles
muggles
muggles
harry
muggles
muggles
harry
NC
NC
muggles
harry
muggles
harry
harry
harry
NC
harry
harry
harry
harry
NC
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
harry
NC
NC
NC
harry
NC
harry
harry
harry
161
Table B16 (continued).
Paragraph
rule 1
rule 2
rule 3
rule 4
rule 5
actual
232
233
234
235
school
harry
harry
harry
school
harry
harry
harry
NC
harry
harry
NC
school
harry
harry
harry
harry
harry
harry
wands
school
harry
harry
harry
162
APPENDIX C
P-BAR WITH TBED SUMMARY
This Table is provided as a quick summary showing results of data obtained by
using P-bar and TBED learning on chapters in a text.
Table C1
P-bar and TBED Learning Summary Data
Blue Carbuncle
Rule #
# Correct
3
148
4
16
5
5
2
2
0
0
Cooper Beaches
Rule # # Correct
3
114
5
19
2
10
1
1
0
0
Engineers Thumb
Rule # # Correct
3
100
4
26
2
9
5
6
1
1
# Poss
216
# Poss
210
# Poss
180
# Correct
171
% Correct
79
# Correct
144
% Correct
69
% Correct
79
Nobel Bachelor
Rule # # Correct
3
138
4
13
2
3
5
2
0
0
Orange Pips
Rule #
# Correct
2
132
5
12
3
3
4
3
1
1
# Poss
232
# Poss
176
% Correct
67
# Correct
156
% Correct
86
# Correct
142
# Correct
151
Red Headded League
Rule #
# Correct
4
155
3
17
5
9
1
5
0
0
# Poss
217
% Correct
86
# Correct
186
163
Table C1 (continued).
Specled Band
Rule #
# Correct
3
162
5
26
2
15
4
4
0
0
Yellow Face
Rule # # Correct
3
107
4
12
5
6
2
2
0
0
The Storm Ch 5
Rule # # Correct
2
20
1
1
5
1
0
0
0
0
# Poss
253
# Poss
155
# Poss
27
# Correct
207
% Correct
82
# Correct
127
% Correct
82
# Correct
22
% Correct
81
The Storm Ch 6
Rule # # Correct
2
45
5
10
4
2
0
0
0
0
HP BK1 CH3
Rule #
# Correct
2
63
5
6
1
4
3
4
0
0
HP BK5 CH13
Rule #
# Correct
2
204
3
2
1
1
5
1
0
0
# Poss
67
# Poss
127
# Poss
235
% Correct
85
# Correct
57
% Correct
61
# Correct
77
% Correct
89
# Correct
208
164
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Conference on Applied Natural Language Processing, ACL, Trento, Italy.
Brill, E. (1995) Transformation-Based Error-Driven Learning and Natural Language
Processing: A Case Study in Part-of-Speech Tagging. Baltimore, MD. The John
Hopkins University.
Chomsky, N. (1970). Remarks on nominalization. Readings in English
transformational grammar, ed. by R. A. Jacobs & P. S. Rosenbaum, 184–221.
Waltham, Massachusetts: Ginn
Perkins, A. L., Gunichetty H., Pachva S., Rishel T., Walley, B., & Satya, C. (2014).
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Rishel, T. (2013) TermTagger [Computer Software]. Long Beach, MS: University of
Southern Mississippi, Gulf Coast.
Sidner, B. J. (1986). Attention, Intentions, and the Structure of Discourse. Computational
Linguistics, 12(3), 175-203.