Word Sense Disambiguation
D. De Cao
R. Basili
Corso di Web Mining e Retrieval
a.a. 2008-9
May 21, 2009
Excerpt of the R. Mihalcea and T. Pedersen AAAI 2005 Tutorial, at:
http://www.d.umn.edu/t̃pederse/Tutorials/ADVANCES-IN-WSD-AAAI-2005.ppt
Definitions
Word sense disambiguation
is the problem of selecting a sense for a word from a set of predefined
possibilities.
Definitions
Word sense disambiguation
is the problem of selecting a sense for a word from a set of predefined
possibilities.
Sense Inventory usually comes from a dictionary or thesaurus.
Knowledge intensive methods, supervised learning, and (sometimes)
bootstrapping approaches.
Definitions
Word sense disambiguation
is the problem of selecting a sense for a word from a set of predefined
possibilities.
Sense Inventory usually comes from a dictionary or thesaurus.
Knowledge intensive methods, supervised learning, and (sometimes)
bootstrapping approaches.
Word sense discrimination
is the problem of dividing the usages of a word into different meanings,
without regard to any particular existing sense inventory.
Definitions
Word sense disambiguation
is the problem of selecting a sense for a word from a set of predefined
possibilities.
Sense Inventory usually comes from a dictionary or thesaurus.
Knowledge intensive methods, supervised learning, and (sometimes)
bootstrapping approaches.
Word sense discrimination
is the problem of dividing the usages of a word into different meanings,
without regard to any particular existing sense inventory.
Unsupervised techniques.
Computers versus Humans
Polysemy: most words have many possible meanings.
Computers versus Humans
Polysemy: most words have many possible meanings.
A computer program has no basis for knowing which one is
appropriate, even if it is obvious to a human ...
Computers versus Humans
Polysemy: most words have many possible meanings.
A computer program has no basis for knowing which one is
appropriate, even if it is obvious to a human ...
Ambiguity is rarely a problem for humans in their day to day
communication, except in extreme cases ...
Computers versus Humans
Polysemy: most words have many possible meanings.
A computer program has no basis for knowing which one is
appropriate, even if it is obvious to a human ...
Ambiguity is rarely a problem for humans in their day to day
communication, except in extreme cases ...
Example:
The fisherman jumped off the bank and into the water.
The bank down the street was robbed!
Brief Historical Overview
Noted as problem for Machine Translation (Weaver, 1949)
A word can often only be translated if you know the specific sense
intended (A bill in English could be a pico or a cuenta in Spanish)
Bar-Hillel (1960) posed the following:
Little John was looking for his toy box. Finally, he found it. The box was
in the pen. John was very happy.
Is “pen” a writing instrument or an enclosure where children play?
... declared it unsolvable, left the field of MT!
Brief Historical Overview
1970s - 1980s
Rule based systems
Rely on hand crafted knowledge sources
1990s
Corpus based approaches
Dependence on sense tagged text
(Ide and Veronis, 1998) overview history from early days to 1998.
2000s
Hybrid Systems
Minimizing or eliminating use of sense tagged text
Taking advantage of the Web
Practical Applications
Machine Translation
Translate bill from English to Spanish
Is it a pico or a cuenta?
Is it a bird jaw or an invoice?
Practical Applications
Machine Translation
Translate bill from English to Spanish
Is it a pico or a cuenta?
Is it a bird jaw or an invoice?
Information Retrieval
Find all Web Pages about cricket
The sport or the insect?
Practical Applications
Machine Translation
Translate bill from English to Spanish
Is it a pico or a cuenta?
Is it a bird jaw or an invoice?
Information Retrieval
Find all Web Pages about cricket
The sport or the insect?
Question Answering
What is George Miller’s position on gun control?
The psychologist or US congressman?
Practical Applications
Machine Translation
Translate bill from English to Spanish
Is it a pico or a cuenta?
Is it a bird jaw or an invoice?
Information Retrieval
Find all Web Pages about cricket
The sport or the insect?
Question Answering
What is George Miller’s position on gun control?
The psychologist or US congressman?
Knowledge Acquisition
Add to KB: Herb Bergson is the mayor of Duluth.
Minnesota or Georgia?
Overview of the Problem
Many words have several meanings (homonymy / polysemy)
Ex: chair - furniture or person
Ex: child - young person or human offspring
Overview of the Problem
Many words have several meanings (homonymy / polysemy)
Ex: chair - furniture or person
Ex: child - young person or human offspring
Determine which sense of a word is used in a specific sentence
Overview of the Problem
Many words have several meanings (homonymy / polysemy)
Ex: chair - furniture or person
Ex: child - young person or human offspring
Determine which sense of a word is used in a specific sentence
Note:
often, the different senses of a word are closely related
Ex: title :
right of legal ownership
document that is evidence of the legal ownership
Overview of the Problem
Many words have several meanings (homonymy / polysemy)
Ex: chair - furniture or person
Ex: child - young person or human offspring
Determine which sense of a word is used in a specific sentence
Note:
often, the different senses of a word are closely related
Ex: title :
right of legal ownership
document that is evidence of the legal ownership
sometimes, several senses can be “activated” in a single context
(co-activation)
Ex: This could bring competition to the trade:
the act of competing
the people who are competing
Word Senses
The meaning of a word in a given context
Word Senses
The meaning of a word in a given context
Word sense representations
With respect to a dictionary
chair = a seat for one person, with a support for the back; he put his coat
over the back of the chair and sat down
chair = the position of professor; he was awarded an endowed chair in
economics
Word Senses
The meaning of a word in a given context
Word sense representations
With respect to a dictionary
chair = a seat for one person, with a support for the back; he put his coat
over the back of the chair and sat down
chair = the position of professor; he was awarded an endowed chair in
economics
With respect to the translation in a second language
chair = chaise
chair = directeur
Word Senses
The meaning of a word in a given context
Word sense representations
With respect to a dictionary
chair = a seat for one person, with a support for the back; he put his coat
over the back of the chair and sat down
chair = the position of professor; he was awarded an endowed chair in
economics
With respect to the translation in a second language
chair = chaise
chair = directeur
With respect to the context where it occurs (discrimination)
“Sit on a chair” “Take a seat on this chair”
“The chair of the Math Department” “The chair of the meeting”
Approaches to Word Sense Disambiguation
Knowledge-Based Disambiguation
use of external lexical resources such as dictionaries and thesauri
discourse properties
Approaches to Word Sense Disambiguation
Knowledge-Based Disambiguation
use of external lexical resources such as dictionaries and thesauri
discourse properties
Supervised Disambiguation
based on a labeled training set
the learning system has:
a training set of feature-encoded inputs AND
their appropriate sense label (category)
Approaches to Word Sense Disambiguation
Knowledge-Based Disambiguation
use of external lexical resources such as dictionaries and thesauri
discourse properties
Supervised Disambiguation
based on a labeled training set
the learning system has:
a training set of feature-encoded inputs AND
their appropriate sense label (category)
Unsupervised Disambiguation
based on unlabeled corpora
The learning system has:
a training set of feature-encoded inputs BUT
NOT their appropriate sense label (category)
All Words Word Sense Disambiguation
Attempt to disambiguate all open-class words in a text
“He put his suit over the back of the chair”
All Words Word Sense Disambiguation
Attempt to disambiguate all open-class words in a text
“He put his suit over the back of the chair”
Knowledge-based approaches
All Words Word Sense Disambiguation
Attempt to disambiguate all open-class words in a text
“He put his suit over the back of the chair”
Knowledge-based approaches
Use information from dictionaries
Definitions / Examples for each meaning
Find similarity between definitions and current context
All Words Word Sense Disambiguation
Attempt to disambiguate all open-class words in a text
“He put his suit over the back of the chair”
Knowledge-based approaches
Use information from dictionaries
Definitions / Examples for each meaning
Find similarity between definitions and current context
Position in a semantic network
Find that “table” is closer to “chair/furniture” than to “chair/person”
All Words Word Sense Disambiguation
Attempt to disambiguate all open-class words in a text
“He put his suit over the back of the chair”
Knowledge-based approaches
Use information from dictionaries
Definitions / Examples for each meaning
Find similarity between definitions and current context
Position in a semantic network
Find that “table” is closer to “chair/furniture” than to “chair/person”
Use discourse properties
A word exhibits the same sense in a discourse / in a collocation
All Words Word Sense Disambiguation
Minimally supervised approaches
Learn to disambiguate words using small annotated corpora
E.g. SemCor - corpus where all open class words are disambiguated
200,000 running words
Most frequent sense
Targeted Word Sense Disambiguation
Disambiguate one target word
“Take a seat on this chair”
“The chair of the Math Department”
Targeted Word Sense Disambiguation
Disambiguate one target word
“Take a seat on this chair”
“The chair of the Math Department”
WSD is viewed as a typical classification problem
use machine learning techniques to train a system
Targeted Word Sense Disambiguation
Disambiguate one target word
“Take a seat on this chair”
“The chair of the Math Department”
WSD is viewed as a typical classification problem
use machine learning techniques to train a system
Training:
Corpus of occurrences of the target word, each occurrence annotated with
appropriate sense
Build feature vectors:
a vector of relevant linguistic features that represents the context (ex: a
window of words around the target word)
Targeted Word Sense Disambiguation
Disambiguate one target word
“Take a seat on this chair”
“The chair of the Math Department”
WSD is viewed as a typical classification problem
use machine learning techniques to train a system
Training:
Corpus of occurrences of the target word, each occurrence annotated with
appropriate sense
Build feature vectors:
a vector of relevant linguistic features that represents the context (ex: a
window of words around the target word)
Disambiguation:
Disambiguate the target word in new unseen text
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Encode information about the words around the target word
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Encode information about the words around the target word
typical features include: words, root forms, POS tags, frequency, ...
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Encode information about the words around the target word
typical features include: words, root forms, POS tags, frequency, ...
An electric guitar and bass player stand off to one side, not really part of the
scene, just as a sort of nod to gringo expectations perhaps.
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Encode information about the words around the target word
typical features include: words, root forms, POS tags, frequency, ...
An electric guitar and bass player stand off to one side, not really part of the
scene, just as a sort of nod to gringo expectations perhaps.
Surrounding context (local features)
[ (guitar, NN1), (and, CJC), (player, NN1), (stand, VVB) ]
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Encode information about the words around the target word
typical features include: words, root forms, POS tags, frequency, ...
An electric guitar and bass player stand off to one side, not really part of the
scene, just as a sort of nod to gringo expectations perhaps.
Surrounding context (local features)
[ (guitar, NN1), (and, CJC), (player, NN1), (stand, VVB) ]
Frequent co-occurring words (topical features)
[fishing, big, sound, player, fly, rod, pound, double, runs, playing, guitar,
band]
[0,0,0,1,0,0,0,0,0,0,1,0]
Targeted Word Sense Disambiguation
Take a window of n word around the target word
Encode information about the words around the target word
typical features include: words, root forms, POS tags, frequency, ...
An electric guitar and bass player stand off to one side, not really part of the
scene, just as a sort of nod to gringo expectations perhaps.
Surrounding context (local features)
[ (guitar, NN1), (and, CJC), (player, NN1), (stand, VVB) ]
Frequent co-occurring words (topical features)
[fishing, big, sound, player, fly, rod, pound, double, runs, playing, guitar,
band]
[0,0,0,1,0,0,0,0,0,0,1,0]
Other features:
[followed by “player”, contains “show” in the sentence, ... ]
[yes, no, ... ]
Unsupervised Disambiguation
Disambiguate word senses:
without supporting tools such as dictionaries and thesauri
without a labeled training text
Unsupervised Disambiguation
Disambiguate word senses:
without supporting tools such as dictionaries and thesauri
without a labeled training text
Without such resources, word senses are not labeled
We cannot say “chair/furniture” or “chair/person”
Unsupervised Disambiguation
Disambiguate word senses:
without supporting tools such as dictionaries and thesauri
without a labeled training text
Without such resources, word senses are not labeled
We cannot say “chair/furniture” or “chair/person”
We can:
Cluster/group the contexts of an ambiguous word into a number of groups
Discriminate between these groups without actually labeling them
Unsupervised Disambiguation
Hypothesis: same senses of words will have similar neighboring words
Unsupervised Disambiguation
Hypothesis: same senses of words will have similar neighboring words
Disambiguation algorithm
Identify context vectors corresponding to all occurrences of a particular
word
Partition them into regions of high density
Assign a sense to each such region
Unsupervised Disambiguation
Hypothesis: same senses of words will have similar neighboring words
Disambiguation algorithm
Identify context vectors corresponding to all occurrences of a particular
word
Partition them into regions of high density
Assign a sense to each such region
“Sit on a chair”
“Take a seat on this chair”
Unsupervised Disambiguation
Hypothesis: same senses of words will have similar neighboring words
Disambiguation algorithm
Identify context vectors corresponding to all occurrences of a particular
word
Partition them into regions of high density
Assign a sense to each such region
“Sit on a chair”
“Take a seat on this chair”
“The chair of the Math Department”
“The chair of the meeting”
Evaluating Word Sense Disambiguation
Metrics:
Precision = percentage of words that are tagged correctly, out of the words
addressed by the system
Recall = percentage of words that are tagged correctly, out of all words in
the test set
Special tags are possible:
Unknown
Proper noun
Multiple senses
Compare to a gold standard
SEMCOR corpus, SENSEVAL corpus, ...
Evaluating Word Sense Disambiguation
Difficulty in evaluation:
Nature of the senses to distinguish has a huge impact on results
Evaluating Word Sense Disambiguation
Difficulty in evaluation:
Nature of the senses to distinguish has a huge impact on results
Coarse versus fine-grained sense distinction
Evaluating Word Sense Disambiguation
Difficulty in evaluation:
Nature of the senses to distinguish has a huge impact on results
Coarse versus fine-grained sense distinction
chair = a seat for one person, with a support for the back; “he put his
coat over the back of the chair and sat down”
chair = the position of professor; “he was awarded an endowed chair
in economics”
Evaluating Word Sense Disambiguation
Difficulty in evaluation:
Nature of the senses to distinguish has a huge impact on results
Coarse versus fine-grained sense distinction
chair = a seat for one person, with a support for the back; “he put his
coat over the back of the chair and sat down”
chair = the position of professor; “he was awarded an endowed chair
in economics”
bank = a financial institution that accepts deposits and channels the
money into lending activities; “he cashed a check at the bank”; “that bank
holds the mortgage on my home”
bank = a building in which commercial banking is transacted; “the bank
is on the corner of Nassau and Witherspoon”
Evaluating Word Sense Disambiguation
Difficulty in evaluation:
Nature of the senses to distinguish has a huge impact on results
Coarse versus fine-grained sense distinction
chair = a seat for one person, with a support for the back; “he put his
coat over the back of the chair and sat down”
chair = the position of professor; “he was awarded an endowed chair
in economics”
bank = a financial institution that accepts deposits and channels the
money into lending activities; “he cashed a check at the bank”; “that bank
holds the mortgage on my home”
bank = a building in which commercial banking is transacted; “the bank
is on the corner of Nassau and Witherspoon”
Sense maps
Cluster similar senses
Allow for both fine-grained and coarse-grained evaluation
Knowledge-based Methods for Word Sense Disambiguation
Knowledge-based WSD = class of WSD methods relying (mainly) on
knowledge drawn from dictionaries and/or raw text
Knowledge-based Methods for Word Sense Disambiguation
Knowledge-based WSD = class of WSD methods relying (mainly) on
knowledge drawn from dictionaries and/or raw text
Resources
Yes
Machine Readable Dictionaries
Raw corpora
No
Manually annotated corpora
Knowledge-based Methods for Word Sense Disambiguation
Knowledge-based WSD = class of WSD methods relying (mainly) on
knowledge drawn from dictionaries and/or raw text
Resources
Yes
Machine Readable Dictionaries
Raw corpora
No
Manually annotated corpora
Scope
All open-class words
Machine Readable Dictionaries
In recent years, most dictionaries made available in Machine Readable
format (MRD)
Oxford English Dictionary
Collins
Longman Dictionary of Ordinary Contemporary English (LDOCE)
Machine Readable Dictionaries
In recent years, most dictionaries made available in Machine Readable
format (MRD)
Oxford English Dictionary
Collins
Longman Dictionary of Ordinary Contemporary English (LDOCE)
Thesauruses - add synonymy information
Roget Thesaurus
Machine Readable Dictionaries
In recent years, most dictionaries made available in Machine Readable
format (MRD)
Oxford English Dictionary
Collins
Longman Dictionary of Ordinary Contemporary English (LDOCE)
Thesauruses - add synonymy information
Roget Thesaurus
Semantic networks - add more semantic relations
WordNet
EuroWordNet
MRD - A Resource for Knowledge-based WSD
For each word in the language vocabulary, an MRD provides:
A list of meanings
Definitions (for all word meanings)
Typical usage examples (for most word meanings)
MRD - A Resource for Knowledge-based WSD
For each word in the language vocabulary, an MRD provides:
A list of meanings
Definitions (for all word meanings)
Typical usage examples (for most word meanings)
WordNet definitions/examples for the noun plant
1
buildings for carrying on industrial labor; “they built a large plant to
manufacture automobiles”
2
a living organism lacking the power of locomotion
3
something planted secretly for discovery by another; “the police used a
plant to trick the thieves”; “he claimed that the evidence against him
was a plant”
4
an actor situated in the audience whose acting is rehearsed but seems
spontaneous to the audience
MRD - A Resource for Knowledge-based WSD
A thesaurus adds:
An explicit synonymy relation between word meanings
MRD - A Resource for Knowledge-based WSD
A thesaurus adds:
An explicit synonymy relation between word meanings
WordNet synsets for the noun plant
1
plant, works, industrial plant
2
plant, flora, plant life
MRD - A Resource for Knowledge-based WSD
A thesaurus adds:
An explicit synonymy relation between word meanings
WordNet synsets for the noun plant
1
plant, works, industrial plant
2
plant, flora, plant life
A semantic network adds:
Hypernymy/hyponymy (IS-A), meronymy/holonymy (PART-OF),
antonymy, entailnment, etc.
MRD - A Resource for Knowledge-based WSD
A thesaurus adds:
An explicit synonymy relation between word meanings
WordNet synsets for the noun plant
1
plant, works, industrial plant
2
plant, flora, plant life
A semantic network adds:
Hypernymy/hyponymy (IS-A), meronymy/holonymy (PART-OF),
antonymy, entailnment, etc.
WordNet related concepts for the meaning plant life - {plant,
flora, plant life}
hypernym: {organism, being}
hypomym: {house plant}, {fungus}, ...
meronym: {plant tissue}, {plant part}
holonym: {Plantae, kingdom Plantae, plant kingdom}
Lesk Algorithm
(Michael Lesk 1986): Identify senses of words in context using definition
overlap
Algorithm:
1
Retrieve from MRD all sense definitions of the words to be
disambiguated
2
Determine the definition overlap for all possible sense combinations
3
Choose senses that lead to highest overlap
Lesk Algorithm: Example
disambiguate PINE CONE
PINE
1
2
kinds of evergreen tree with needle-shaped leaves
waste away through sorrow or illness
CONE
1
2
3
solid body which narrows to a point
something of this shape whether solid or hollow
fruit of certain evergreen trees
Lesk Algorithm: Example
disambiguate PINE CONE
PINE
1
2
kinds of evergreen tree with needle-shaped leaves
waste away through sorrow or illness
CONE
1
2
3
solid body which narrows to a point
something of this shape whether solid or hollow
fruit of certain evergreen trees
Pine#1 ∩ Cone#1 = 0
Pine#2 ∩ Cone#1 = 0
Pine#1 ∩ Cone#2 = 1
Pine#2 ∩ Cone#2 = 0
Pine#1 ∩ Cone#3 = 2
Pine#2 ∩ Cone#4 = 0
Lesk Algorithm for More than Two Words?
I saw a man who is 98 years old and can still walk and tell jokes
Lesk Algorithm for More than Two Words?
I saw a man who is 98 years old and can still walk and tell jokes
nine open class words: see(26), man(11), year(4), old(8), can(5),
still(4), walk(10), tell(8), joke(3)
Lesk Algorithm for More than Two Words?
I saw a man who is 98 years old and can still walk and tell jokes
nine open class words: see(26), man(11), year(4), old(8), can(5),
still(4), walk(10), tell(8), joke(3)
43,929,600 sense combinations! How to find the optimal sense
combination?
Lesk Algorithm for More than Two Words?
I saw a man who is 98 years old and can still walk and tell jokes
nine open class words: see(26), man(11), year(4), old(8), can(5),
still(4), walk(10), tell(8), joke(3)
43,929,600 sense combinations! How to find the optimal sense
combination?
Simulated annealing (Cowie, Guthrie, Guthrie 1992)
Define a function E = combination of word senses in a given text.
Find the combination of senses that leads to highest definition overlap
(redundancy)
Lesk Algorithm for More than Two Words?
I saw a man who is 98 years old and can still walk and tell jokes
nine open class words: see(26), man(11), year(4), old(8), can(5),
still(4), walk(10), tell(8), joke(3)
43,929,600 sense combinations! How to find the optimal sense
combination?
Simulated annealing (Cowie, Guthrie, Guthrie 1992)
Define a function E = combination of word senses in a given text.
Find the combination of senses that leads to highest definition overlap
(redundancy)
1
2
3
Start with E = the most frequent sense for each word
At each iteration, replace the sense of a random word in the set with a
different sense, and measure E
Stop iterating when there is no change in the configuration of senses
Lesk Algorithm: A Simplified Version
Original Lesk definition:
measure overlap between sense definitions for all words in context.
Identify simultaneously the correct senses for all words in context
Lesk Algorithm: A Simplified Version
Original Lesk definition:
measure overlap between sense definitions for all words in context.
Identify simultaneously the correct senses for all words in context
Simplified Lesk (Kilgarriff & Rosensweig 2000):
measure overlap between sense definitions of a word and current context
Identify the correct sense for one word at a time
Search space significantly reduced
Algorithm for simplified Lesk:
1
Retrieve from MRD all sense definitions of the word to be
disambiguated
2
Determine the overlap between each sense definition and the current
context
3
Choose the sense that leads to highest overlap
Example of simplified Lesk
disambiguate PINE in
“Pine cones hanging in a tree”
PINE
1
kinds of evergreen tree with needle-shaped leaves
2
waste away through sorrow or illness
Example of simplified Lesk
disambiguate PINE in
“Pine cones hanging in a tree”
PINE
1
kinds of evergreen tree with needle-shaped leaves
2
waste away through sorrow or illness
Pine#1 ∩ Sentence = 1
Pine#2 ∩ Sentence = 0
Evaluations of Lesk Algorithm
Initial evaluation by M. Lesk
50-70% on short samples of text manually annotated set, with respect to
Oxford Advanced Learner’s Dictionary
Simulated annealing
47% on 50 manually annotated sentences
Evaluation on Senseval-2 all-words data, with back-off to random sense
(Mihalcea & Tarau 2004)
Original Lesk: 35%
Simplified Lesk: 47%
Evaluation on Senseval-2 all-words data, with back-off to most frequent
sense (Vasilescu, Langlais, Lapalme 2004)
Original Lesk: 42%
Simplified Lesk: 58%
Semantic Similarity
Words in a discourse must be related in meaning, for the discourse to be
coherent (Haliday and Hassan, 1976)
Use this property for WSD - Identify related meanings for words that
share a common context
Semantic Similarity
Words in a discourse must be related in meaning, for the discourse to be
coherent (Haliday and Hassan, 1976)
Use this property for WSD - Identify related meanings for words that
share a common context
Context span:
1
2
Local context: semantic similarity between pairs of words
Global context: lexical chains
Semantic Similarity in a Local Context
Similarity determined between pairs of concepts, or between a word
and its surrounding context
Relies on similarity metrics on semantic networks (Rada et al. 1989)
Decision List for WSD (Yarowsky, 1994)
Identify collocational features from sense tagged data.
Word immediately to the left or right of target :
I have my bank/1 statement.
The river bank/2 is muddy.
Pair of words to immediate left or right of target :
The world’s richest bank/1 is here in New York.
The river bank/2 is muddy.
Words found within k positions to left or right of target, where k is
often 10-50 :
My credit is just horrible because my bank/1 has made several mistakes
with my account and the balance is very low.
Decision List for WSD (Yarowsky, 1994)
Sort order of collocation tests using log of conditional probabilities.
Words most indicative of one sense (and not the other) will be ranked
highly.
Decision List for WSD (Yarowsky, 1994)
Sort order of collocation tests using log of conditional probabilities.
Words most indicative of one sense (and not the other) will be ranked
highly.
p(S = 1|Fi = Collocationi )
Abs log
p(S = 2|Fi = Collocationi )
Decision List for WSD (Yarowsky, 1994)
Decision List for WSD (Yarowsky, 1994)
Decision List for WSD (Yarowsky, 1994)
References I
(Gale, Church and Yarowsky 1992) Gale, W., Church, K., and
Yarowsky, D. Estimating upper and lower bounds on the performance
of word-sense disambiguation programs ACL 1992.
(Miller et. al., 1994) Miller, G., Chodorow, M., Landes, S., Leacock,
C., and Thomas, R. Using a semantic concordance for sense
identification. ARPA Workshop 1994.
(Miller, 1995) Miller, G. Wordnet: A lexical database. ACM, 38(11)
1995.
(Senseval) Senseval evaluation exercises http://www.senseval.org
(Agirre and Rigau, 1995) Agirre, E. and Rigau, G. A proposal for word
sense disambiguation using conceptual distance. RANLP 1995.
(Banerjee and Pedersen 2002) Banerjee, S. and Pedersen, T. An adapted
Lesk algorithm for word sense disambiguation using WordNet.
CICLING 2002.
References II
(Cowie, Guthrie and Guthrie 1992), Cowie, L. and Guthrie, J. A. and
Guthrie, L.: Lexical disambiguation using simulated annealing.
COLING 2002.
(Jiang and Conrath 1997) Jiang, J. and Conrath, D. Semantic similarity
based on corpus statistics and lexical taxonomy. COLING 1997.
(Lesk, 1986) Lesk, M. Automatic sense disambiguation using machine
readable dictionaries: How to tell a pine cone from an ice cream cone.
SIGDOC 1986.
(Lin 1998) Lin, D An information theoretic definition of similarity.
ICML 1998.
(Mihalcea, Tarau, Figa 2004) R. Mihalcea, P. Tarau, E. Figa PageRank
on Semantic Networks with Application to Word Sense Disambiguation,
COLING 2004.
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