（JSCL-2007）
基于句子特征分析和模糊推断的中文事件摘要实现机制
周凯，李芳
（上海交通大学计算机科学与工程系，上海 200240）
摘要：本文提出了基于句子特征分析和模糊推断的事件新闻摘要实现机制。其主要思想是首先以事件新闻中的句子为单元，根
据特征计算句子的权重，并且按照权重大小对句子进行排序；然后使用模糊推断技术分析各个句子与事件的内在关联性；最后选
择其中权重较大、关联性较强的句子来生成事件摘要。在中文突发事件新闻语料库上进行了实验，效果良好，结果表明该方法能
够有效地对中文突发事件新闻自动生成摘要。
关键词：突发事件；句子特征；概念结构；模糊推断；新闻摘要
中图分类号：TP391
文献标识码：A
The Design and Implementation of Chinese Event Summarization
Based on Sentence Features and Fuzzy Inference
ZHOU Kai, LI Fang
(Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai 200240)
Abstract: This paper proposes a novel sentence extraction method for Chinese event summarization, which integrates the domainindependent feature-based sentence scoring method and the domain-specific fuzzy inference mechanism. First it assigns a reasonable score
to each sentence by analyzing the features of the sentence. Then it utilizes fuzzy inference mechanism to infer the semantic relevance
between each sentence and the event topic. Finally it extracts both important and domain-related sentences to generate an event summary.
Experiment is conducted on our collection of Chinese Event News and the results indicate that our approach outperforms the baseline. In
particular, it can effectively summarize Chinese accidental events.
Key words: Accidental events; sentence features; conceptual structure; fuzzy inference; summary
1. INTRODUCTION
2. RELATED WORK
Due to the amount of online documents, automatic high quality
summaries have become important in order to provide concise
information for some events. The goal of event summarization is
to extract important and relevant information from online news
reports and present an event to the user in a condensed form.
There are many researches on document summarization in
recent years. Edmundson [1] proposed an extractive
summarization method based on sentence features. Inspired by his
work, Nobata [2] evaluated different features for sentence
extraction and proposed a robust summarization system, which
considered interior structural characteristics of documents.
Carbonell and Goldstein [3] reorganized documents and produced
summaries by using Maximal Marginal Relevance (MMR)
method. Based on his work, Nomoto [4] proposed an approach by
exploiting the diversity of concepts in context for summarization.
Most of these previous methods generate summaries by analyzing
the interior characteristics of documents, which is domainindependent. However, they have performance limitations for
special requirements.
Most of the conventional sentence extraction summarization
methods either extract important sentences from unstructured
documents, or draw relevant sentences according to some specific
knowledge. The former methods extract information by
maximizing the features of documents, which is domainindependent. The latter methods extract information by
maximizing the relevance between documents and some domain
knowledge. However, some domain-independent methods may
extract important but irrelevant sentences, while some domainspecific methods may extract some relevant but unimportant
sentences.
This paper proposes a novel sentence extraction method for
event summarization. It integrates the domain-independent
feature-based sentence scoring method and the domain-specific
fuzzy inference mechanism. Experimental results indicate that it
can effectively summarize Chinese accidental event. The
remainder of this paper is organized as follows. Section 2 presents
the related work. Section 3 describes the sentence extraction
mechanism. Experiments are conducted in Section 4. Section 5
discusses the conclusions and future work.
In this way, some specific knowledge is introduced in order to
improve the quality of summarizations. Barzilay and Elhadad [5]
introduced a lexical chain structure for text summarization based
on Wordnet 1 resource. Varadarajan and Hristidis [6] proposed an
automatic query-specific summarization method for web pages
which employed a form of document graph structure. Zhang et al.
[7] utilized the user's annotations to produce a personalized
summary. Most of these methods select, measure and aggregate
information of documents and generate summaries by using the
information provided by some specific knowledge, such as user’s
queries, domain dictionary and ontology etc.
1
http://wordnet.princeton.edu/online/
Event Query
Sentence List of an event
Web
Event News Retriever
Sentence Features Calculation
CEN Corpus
Event News
Documents
Document
Preprocessing
Domain Glossaries
Conceptual Structure
Feature-based Sentence Scoring
Event
Summaries
Fuzzy Sentence
Extraction Mechanism
How-Net
Fuzzy Rules
Figure 1. The Structure of Our Sentence Extraction Method
3. OUR METHOD
According to the sentence extraction summarization methods
[2], [3], [8], an event summary is defined as a set of sentences
extracted from a document, which covers the most important and
relevant information of that document.
Our method combines the domain-independent feature-based
sentence scoring method and the domain-specific fuzzy inference
mechanism [9], [10] to extract those important and relevant
sentences. Figure 1 shows the entire structure of the proposed
method. If users input a query referring to a specific event, it will
preprocess the event reports and convert each document into a
sentence list. Then, it assigns a feature-based score to a sentence.
The higher the score is, the more important the sentence will be.
The fuzzy inference mechanism was used to infer the semantic
relevance between each sentence and the event topic in order to
extract the relevant sentences. Finally both important and domainrelated sentences are extracted to form an event summary.
3.1 Feature-based Sentence Scoring
Feature-based sentence scoring is built upon four features of a
sentence: the position (pt), the length (len), the weights of terms
in the sentence (wt) and the similarity between the sentence and
the headline (sim).
Let S = {S1,S2,...,Sn} be a sentence list of a document, Si is the
ith sentence, length (Si ) is the length of Si in number of noun
terms, tf ( S , t ) is the term frequency of t in S and sf (t ) is the
sentence frequency in number of sentences which contain term t.
The beginning and the end sentences of a document are
considered more important than those middle ones. The position
feature (pt) of a sentence is defined as the inverse of the minimum
between the positions from the beginning and from the end of the
document.
Score pt ( Si ) =
1
(1 ≤ i ≤ n )
min( i , n − i + 1)
Katz [11] found that the frequencies of content words and
phrases do not grow proportionally with length of the document.
Inspired by his work, the length feature (len) of a sentence is
defined as the number of nouns. This feature awards a sentence if
it has at least C nouns, otherwise returns a negative value as a
penalty. C is defined as the number of nouns in the headline.
Score len ( Si ) =
length ( Si ) − C
length ( Si )
The third feature (wt) of a sentence is defined based on the term
frequency tf ( Si, t ) and sentence frequency sf (t ) .
Score wt ( Si ) =
S
∑ tf (S , t ) log sf (t )
i
t∈Si
where | ⋅ | is the cardinality function. This feature utilizes the raw
term frequencies to measure the significance of a sentence. It
awards a sentence if the sentence contains more important terms.
The last feature (sim) of a sentence is defined as the similarity
between a sentence and the headline in the document. It is
calculated by using the Cosine function as follows:
Score sim ( S i , h ) =
∑
∑
m
j =1
m
j =1
W ij W hj
(W ij ) 2
∑
m
j =1
(W hj ) 2
where h denotes the headline of the document and Whj is the
weight of the jth term in h .
Based on the four features, a sentence will be scored according
to the following formula (1):
Scorefea( Si ) = α ⋅ Scorept ( Si ) +
β ⋅ Scorelen( Si ) + γ ⋅ Scorewt ( Si ) + η ⋅ Scoresim( Si )
(1)
where α , β , γ ,η determine the effect of each feature on the score
for a sentence. They are set as 0.2, 0.1, 0.2 and 0.5 respectively.
3.2 Fuzzy Sentence Extraction Mechanism
The fuzzy inference mechanism is based on conceptual
structure and semantic analysis in order to extract the relevant
sentences.
3.2.1 Conceptual Structure
Conceptual structure is a top-level ontology which describes
different knowledge in nature. It plays an important role in many
applications since it can imply crucial hierarchy information
about concepts [12]. In this paper, a sememe-based conceptual
structure is proposed in order to reflect the hierarchy relations
about the concepts within sentences. It consists of 1612 sememes
derived from HowNet [13]. It is manually constructed by using
Protégé-2000 [14] tool based on OWL_DL 2 working description
language. Figure 2 shows a part of the conceptual structure. It
implies affiliation relations of concepts vertically and distinctions
of concepts horizontally.
Hylanda Chinese POS tagger 3 is used for word segmentation.
There are 30 kinds of POS tags defined in the tagger but only 9 of
them are preserved. Table 1 shows the preserved tags and
examples. Since this tagger allows a user dictionary, a domain
glossary (explained later) is considered to improve its matching
performance. After word segmentation, each Chinese term is
assigned a POS tag. The TPS variable, i.e. the POS relevance
strength of a Chinese term pair, is defined as the distance between
two POS tags in POS tree (Figure 3). Figure 3 shows the structure
of the POS tree. The leaves denote the preserved POS tags: N
(common noun), NR (name noun), NT (organization), NX
(English characters), NZ (proper noun), F (direction noun), NS
(place noun), S (location noun) and T (time noun). The inner
nodes divide POS tags into different branches: Pa denotes
common nouns; Pb contains Pe and proper nouns; Pc represents
nouns about directions, places or locations; Pd denotes time nouns;
and Pe represents names, organizations and English characters.
They are used to determine the degree of relations among
different leaves. Suppose two terms have NR and NS POS tags
respectively, the distance between them is 5, since a path
NR→Pe→Pb→Pc→NS is found. In particular, the distance
between two POS tags in POS tree is document-independent, they
can be precomputed.
Figure 3. The Structure of POS Tree
Figure 2. The Structure of Conceptual Structure
Table 1. Chinese POS tags by Hylanda
3.2.2 Semantic Analysis on a Chinese Term Pair
Tag
N
NR
NT
NX
A fuzzy variable named semantic relevance strength (SRS) is
used to measure the semantic relevance of a Chinese term pair. It
is calculated by integrating other four fuzzy variables: term
lexical relevance strength (TLS), term part-of-speech (POS)
relevance strength (TPS), term common sememe relevance
strength (TCS) and term relation relevance strength (TRS). They
are used to describe different semantic relations between a
Chinese term pair.
The TLS variable, i.e. the lexical relevance strength of a
Chinese term pair, is defined as the number of common characters
that two terms share, since two terms having common characters
are semantically similar. For instance, as for a Chinese term pair
伤 亡 人 数 (injured and death number) and 死 亡 人 数 (death
number), the TLS variable of this term pair is 3, since they share 3
common characters.
2
http://www.w3.org/2004/OWL/
NZ
F
NS
S
T
POS
Common Noun
Name
Organization
English
Characters
Proper Noun
Direction Noun
Place Noun
Location
Time
Example
海啸 (tsunami)
韩正 (Han Zheng)
清华 (Tsinghua University)
Jacob
电邮 (E-mail)
之前 (before)
上海市 (Shanghai)
海外 (oversea)
一月 (January)
In addition, HowNet [13] is a knowledge base which describes
concepts by using several sememes which are the minimal
language units in HowNet [13]. The TCS variable, i.e. the
common sememe relevance strength of a Chinese term pair, is
defined as the number of common sememes that two terms share.
3
http://www.hylanda.com/
Take three Chinese terms namely 震 级 (magnitude), 震 中
(epicenter) and 震 源 (focus) as a example. The sememes for
description provided by HowNet [13] are as follow:
震级 ← {属性( attribute), 强度(intensity), 地震(earthquake)}
震中 ← {位置( location), 根( base), 地震( earthquake) }
震源 ← {位置( location), 根( base), 地震( earthquake) }
In this case, 震中(epicenter) and 震源(focus) share 3 sememes
while 震级(magnitude) and 震中 (epicenter) only share 1 sememe.
It is easy to determine that 震中(epicenter) and 震源 (focus) are
more conceptually related. In addition, the sememes provided by
HowNet [13] for a Chinese term have different effects with
respect to their positions. In general, the former sememes are
more important and descriptive than the latter ones. A set of
decreasing weights in [0, 1] is given to sememes associated with
each Chinese term. It is defined as the inverse of the position from
the first sememe. As for 震级 (magnitude), the weight for 属性
(attribute) is 1, the weight for 强 度 (intensity) is 0.5 and the
weight for 地震 (earthquake) is 0.33.
A sememe-based conceptual structure is proposed in section
3.2.1 to describe the hierarchy relations about the concepts within
sentences. Based on the sememe-based conceptual structure, the
TRS variable, i.e. the relation relevance strength of a Chinese
term pair, is defined as the weighted mean distance of all sememe
pairs. For the above three Chinese terms, the distance between 属
性(attribute) and 位置(location) in the conceptual structure is 11
while the distance between 强度 (intensity) and 根 (base) is 9.
Suppose the weight for the first sememe is 1 and the second is 0.3.
Thus the TRS value for 震中 (epicenter) and 震源 (focus) is 0,
while the value for 震级(magnitude) and 震中 (epicenter) is 13.7.
It is reasonable to observe that 震中(epicenter) and 震源 (focus)
are stronger connected in concept property. Also, the distance
between two sememes in the conceptual structure is documentindependent, they can be precomputed.
3.2.3 Fuzzy Inference Mechanism for Semantic
Relevance Computing
After describing the sememe-based conceptual structure and
four fuzzy variables of a Chinese term pair, this section will
discuss how to calculate the semantic relevance strength (SRS) of
a Chinese term pair by using the fuzzy inference mechanism [15].
Due to its great capabilities of accurately simulating human
reasoning in handling uncertainty information, it has been widely
used in many fields including natural language processing [16].
Kuo et al. [9] proposed a three-layered fuzzy inference
mechanism called reinforcement fuzzy neural network with
distributed prediction scheme (RFNN-DPS). Lee et al. [10]
simplified RFNN_DPS and presented an ontology-based fuzzy
event extraction agent for Chinese e-news summarization. Figure
4 shows the interior structure of the fuzzy inference mechanism
which consists of three layers: premise layer, rule layer and
conclusion layer. There are two kinds of nodes namely fuzzy
linguistic nodes and rule nodes. A fuzzy linguistic node denotes a
fuzzy variable and manipulates the information related to that
linguistic variable. A rule node represents a fuzzy rule and
determines the final firing strength of that rule during the
inferring process.
Figure. 4 The structure of fuzzy inference mechanism
The premise layer is built upon fuzzy linguistic nodes in
normal operation mode, called condition nodes. Each condition
node is denoted as a linguistic variable and has a set of linguistic
terms for description of the linguistic variable. There are two
linguistic terms like TLS.Low, TLS.High defined for TLS variable,
two linguistic terms like TPS.Low, TPS.High for TPS variable,
three linguistic terms like TCS.Low, TCS.Mid, TCS.High for TCS
variable, three linguistic terms like TRS.Low, TRS.Mid, TRS.High
for TRS variable and five linguistic terms like SRS.VeryLow,
SRS.Low, SRS.Mid, SRS.High, SRS.VeryHigh for SRS variable.
The input of premise layer is the vector of four fuzzy variables of
a Chinese term pair In (Ti,T j ) = (TLS ij,TPS ij,TCS ij,TRS ij ) . The
output of premise layer is the matching degree between input
vector and rule premises given as follows:
1
1
μ 1 = ((U ijTLS
.Low , U ijTLS . High ),
1
1
(U ijTPS
. High , U ijTPS .Low ),
1
1
1
(U ijTCS
. Low , U ijTCS .Mid , U ijTCS . High ),
1
1
1
(U ijTPS
. High , U ijTPS .Mid , U ijTPS .Low ))
where U 1 ijTLS .Low denotes the matching degree of the TLS.Low
linguistic term of the TLS condition node and is evaluated by
triangular and the trapezoidal membership function.
Figure 5. The Fuzzy Logic Rules
In the rule layer, each node is a rule node, which describes the
relation between the premise part and conclusion part of a fuzzy
rule. The incoming links of each rule node perform precondition
matching of the fuzzy logic rule. The output of it represents the
global matching degree of the fuzzy rule and is linked to
associated fuzzy linguistic nodes in the next layer. 36 fuzzy rules
are obtained from the fuzzy rules in [10] and the corpus for fuzzy
inference. Figure 5 shows some fuzzy logic rules. The premise
part of a fuzzy rule combines four logic statements connected
by AND connectives. The conclusion part of a fuzzy rule outputs
the firing strength of SRS into the next layer. For instance, the
output for fuzzy rule R1 is the firing strength of the first fuzzy
rule given as follows:
μ R2 1 = w1U 1ijTLS .Low
1
+ w2U ijTPS
.Low
+
1
w3U ijTCS
.Low
1
+ w4U ijTRS
.Low
4
∑
and
wi = 1
i =1
where wi is the weight of the ith fuzzy variable and used to
regulate the effect of the fuzzy variable. It is optimized by
maximizing the semantic relevance between arbitrary related
Chinese term pair in the training corpus.
The conclusion layer is also built upon fuzzy linguistic nodes in
reverse operation mode, called conclusion nodes. Each conclusion
node consists of three parts: the input, the match part and the
defuzzifier part. The input of a conclusion node is the firing
strengths of fuzzy rules. The match part performs the fuzzy OR
operation to integrate the fuzzy rules which have the same
consequence. The defuzzifier part performs defuzzification in
order to make a conclusion. The center of area (COA) function is
employed for defuzzification. The final output of conclusion layer
is denoted as the value of SRS fuzzy variable for a Chinese term
pair, which integrates all inference results with their firing
strengths.
R
SRS (Ti , Tj ) =
LT
∑∑ μ
To evaluate quality of this method, a collection of Chinese
Event News (CEN) is built up. It consists of two categories of
event news namely natural disasters (ND) and human-caused
events (HCE). HCE refers to those events aroused by human
beings including Misprision Events, Crime, Traffic Accidents and
Terrorist Events. ND denotes natural disaster events including
Earthquake, Volcano Eruption, Disease, Typhoon and Tsunami.
In addition, a relative summary is constructed for each event item
by manually extracting important and relevant sentences from the
description documents. To sum up, there are 373 pairs of
documents and manually constructed summaries for HCE, and
other 432 pairs for NDE. 25% of them are used for training, and
the rest for test.
4.2 Evaluation Design and Measures
F-measure is defined as an evaluation measure which integrates
precision and recall measures. Precision is defined as the ratio of
the number of correct extracted sentences with the total number of
correct sentences. Recall is the ratio of the number of correct
extracted sentences with the total number of sentences within the
documents. F-measure is given as follows:
2 ∗ Precision ∗ Recall
Precision + Recall
the number of correc t sentence s by syste m
Precision =
the total number of correct se ntences
r =1 s =1
R LT
2
rs
r =1 s =1
where R is the set of 36 fuzzy rules, LT is the set of output
linguistic terms, and G rs is the gravity of sth output linguistic term
associated with rth fuzzy rule node.
3.2.4 Relevant Sentence Extraction
There are two kinds of events: natural disasters and humancaused events. For each type, a domain glossary is constructed by
manually deriving Chinese terms from the corpus. In order
estimate the relevant sentences, a semantic relevance score is
defined as the total semantic relevance strengths among the entire
Chinese term pairs between the sentence and the glossary as
follows:
Score srs ( Si , Glossary ) =
4. EXPERIMENT
4.1 Chinese Event News Corpus
F=
2
rs Grs
∑∑ μ
where a and b are adjustable coefficients. They are set as 0.3 and
0.7 respectively. A threshold is defined and only those sentences
with total score above the threshold is extracted to form an event
summary.
∑ ∑ SRS (T , T )
p
q
(2)
Tp∈Si Tq∈Glossary
where Glossary denotes the domain glossary for an event type,
Tp is a Chinese term in Si , and Tq is the Chinese term in the
glossary. For fuzzy sentence extraction, the total score of a
sentence is defined as a linear function by integrating the sentence
feature score (1) and the semantic relevance score (2) as follows.
TotalScore ( S i ) = a ∗ Score fea ( S i ) + b ∗ Score srs ( Si , Topic ) (3)
Re call =
the number of correc t sentence s by syste m
the total number of sentences by system
In addition, a set of compression ratio is used to calculate the
total number of sentences to be extracted. It is specified as 10%,
20% and 30%. To compare our method with other approaches on
CEN corpus, the feature-based sentence extraction approach by
Nobata [2] is implemented and chosen as a baseline. Nobata [2]
evaluated different features for sentence extraction and proposed
a robust summarization system, which considered interior
structural characteristics of documents.
4.3 Average Performance on CEN
Our experiments consist of two parts. First it evaluates the
overall performance of our method. Then it compares its
performance with the baseline. Table 2 shows the average
performance of our method on CEN corpus. The figures in the
table are the values of F-measure. Experimental results indicate
that more and more desired sentences are extracted proportionally
with compression ratio for many events. But the figures of Crime
and Typhoon events decline from the compression ratio of 20%.
This is because the feature-based sentence scoring part nominates
many sentences which are irrelevant to the specific domain. The
maximum F-measure is 0.5267 for Typhoon events at the
compression ratio of 20%, while the minimum value is 0.3222 for
Terrorist events at 10%.
Table 2. Average Experiment Results on CEN
Compression
Event Type
Misprision Events
Crime
Traffic Accidents
Terrorist Events
Earthquake
Volcano Eruption
Typhoon
Tsunami
Disease Outbreak
Average
10%
20%
30%
0.3742
0.3988
0.4362
0.3222
0.3915
0.3374
0.3518
0.3452
0.4589
0.3796
0.4445
0.4120
0.5011
0.3880
0.4661
0.3998
0.5267
0.3911
0.5255
0.4505
0.4837
0.3874
0.4906
0.4560
0.5144
0.4120
0.4611
0.4538
0.5214
0.4645
In addition, another experiment is conduct to compare the
performances of our method and the baseline. It evaluates two
methods at four particular compression ratios, such as 10%, 20%,
30% and 50%. Figure 6 shows the average experimental results of
comparison between two methods on CEN corpus. It is easy to
observe that our approach outperforms the baseline. The
differences of performance between two methods become distinct
proportionally with compression ratio from 10% to 30%. This is
because fuzzy inference mechanism can extract more relevant
sentences. But from 30% to 50%, both performances decline,
since more and more redundant information are extracted. To sum
up, our method performs much better than the baseline, especially
at the compression ratio of 30%. It means our method can extract
more important and domain-relevant sentences than the baseline.
be used to improve performance of the fuzzy inference
mechanism [9]. In addition, there are some drawbacks by using
the sentence extraction mechanism for summarization. The
summaries may contain redundancy or repetitive information.
Thus how to efficiently improve the quality of summaries by
using information fusion techniques [19] and tackle those
drawbacks would be another interesting issue.
6. REFERENCES
[1] Edmundson, H. New methods in automatic abstracting.
Journal of ACM, 16(2):264-285, 1969.
[2] Nobata, C., Sekine, S., and Isahara, H. Evaluation of
Features for Sentence Extraction on Different Type of
Corpora. In Proceedings of the ACL 2003 workshop on
Multilingual summarization and question answering,
(12):29-36, 2003.
[3] Carbonell, J., and Goldstein, J. The Use of MMR, DiversityBased Reranking for Reordering Documents and Producing
Summaries. In Proceedings of 21th Annual International
ACM/SIGIR Conference on Research and Development in IR,
335-336, 1998. Melbourne, Australia.
[4] Nomoto, T., and Matsunoto, Y. A New Approach to
Unsupervised Text Summarization. In Proceedings of the
24th annual international ACM SIGIR conference on
Research and development in information retrieval SIGIR
'01, 26-34, 2001. New Orleans, Louisiana, United States.
[5] Barzilay, R. and Elhadad, M. Using lexical chains for text
summarization. ISTS, 1997.
[6] Varadarajan, R., and Hristidis, V. A System for QuerySpecific Document Summarization. In Proceedings of the
15th ACM international conference on Information and
knowledge management table of contents CIKM '06, 622631, 2006. Arlington, Virginia, USA.
[7] Zhang, H. Q., Ma, Z. C., Cai, Q. S. A study for documents
summarization based on personal annotation. In Proceedings
of the HLT-NAACL 03 on Text summarization workshop Volume 5, 41-48, 2003. Morristown, NJ, USA.
[8] Hirao, T., Isozaki, H., Maeda, E., and Matsumoto, Y.
Extracting important sentences with support vector machines.
In Proceedings of the 19th international conference on
Computational linguistics, (1):1-7, 2002. Taipei, Taiwan.
Figure. 6 The Comparison Experimental Result on CEN
5. CONCLUSION AND FUTURE WORK
This paper presents a novel sentence extraction method for
event news summarization. The novelty lies in the integration of
the domain-independent feature-based sentence scoring technique
and the domain-specific fuzzy inference mechanism. Evaluation
experiments are conducted on the CEN corpus. The experimental
results indicate that our method outperforms the baseline and can
effectively summarize Chinese event news. However, this method
only simply utilizes the fuzzy inference mechanism to calculate
the semantic relevance of a Chinese term pair for sentence
extraction. In fact, the feedback of this action is very useful. It can
[9] Kuo, Y. H., Hsu, J. P., and Wang, C. W. A Parallel Fuzzy
Inference model with distributed prediction scheme for
reinforcement learning. IEEE Transaction on Fuzzy Systems,
Man, and Cybernetics-Part B: Cybernetics, 28(2), April,
1998.
[10] Lee, C. S., Chen, Y. J., Jian, Z. W. Ontology-based fuzzy
event extraction agent for Chinese e-news summarization.
Expert Systems with Applications 25 (2003) 431–447.
[11] Katz, S. M. Distribution of content words and phrases in text
and language modeling. Natural Language Engineering,
2(1):15-59, 1996.
[12] Zhu, J. H., Hong, J., and Hughes, J. G. Page Cluster: Mining
conceptual link hierarchies from Web log files for adaptive
Web site navigation. ACM Transactions on Internet
Technology (TOIT), 4(2):185-208, 2004. New York, NY,
USA.
[13] DONG, Z. D et al., Related to HowNet, Available From
http://www.keenage.com/html/paper.html
[14] Horridge, M., Knublauch, H., Rector, A., Stevens, R., Wroe,
C. A Practical Guide to Building OWL Ontologies Using the
Protégé-OWL Plug-in and CO-ODE Tools Edition 1.0, URL
Accessed on August 27, 2004.Noy, N.F., Sintek, M., Decker,
S., Crubezy, M., Fergerson, R.W., and Musen, M.A.
Creating semantic web contents with Protege-2000, IEEE
Intelligent Systems 16 (2) (2001) 60–71.
[15] Mamdani, E. H. and Assilian, S. An experiment in linguistic
synthesis with a fuzzy logic controller. International
Journal of Man-Machine Studies, 7(1): 1-13, 1975.
[16] Leondes, C. T. Fuzzy Theory Systems Techniques and
Applications Volume 1, 160-186, 1999. Academic press.
[17] Lin, C. T., Yeh, C. M., Liang, S. F., Chung, J. F., and Kumar,
N. Support-Vector-Based Fuzzy Neural Network for Pattern
Classification. IEEE Transaction on Fuzzy Systems, 14(1),
February, 2006.
[18] Lin, C.T., and Lee, C.S.G. Neural Network based Fuzzy
Logic Control and Decision System, IEEE Transactions on
Computer, 40(12):1320-1336, 1991.
[19] Barzilay, R., McKeown, K. R., and Elhadad, M. Information
fusion in the context of multi-document summarization. In
Proceedings of the 37th annual meeting of the Association
for Computational Linguistics on Computational Linguistics,
550-557, June 20-26, 1999, College Park, Maryland.