TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD
VS. CLASSICAL TF-IDF METHOD FOR WEB INFORMATION
EXTRACTION
Jorge Ropero, Ariel Gómez, Carlos León, Alejandro Carrasco
Department of Electronic Technology,University of Seville, Seville, Spain
Keywords: Term weighting, TF-IDF, Fuzzy logic, Information extraction, Information retrieval, Vector space model,
Intelligent agent.
Abstract: Solving Term Weighting problem is one of the most important tasks for Information Retrieval and
Information Extraction. Tipically, the TF-IDF method have been widely used for determining the weight of
a term. In this paper, we propose a novel alternative fuzzy logic based method. The main advantage for the
proposed method is the obtention of better results, especially in terms of extracting not only the most
suitable information but also related information. This method will be used for the design of a Web
Intelligent Agent which will soon start to work for the University of Seville web page.
1 INTRODUCTION
The great amount of available information caused by
the rising of Information Technology constitutes an
enormous advantage when it comes to search for
needed information. However, at the same time, it is
a great problem to distinguish the necessary
information among the huge quantity of
unneccessary data.
For this reason, the concepts of Information
Retrieval (IR) and Information Extraction (IE) came
up. IR is a field in which there have been great
advances in the last decades (Kwok, 1989),
especially in what concerns to the search of
documents. Nevertheless, IR does not only come
down to document searching. IR tools may be used
for the objects in any collection of accumulated
knowledge such as the objects stored in a shop or the
photographies in an album. The generalization of
this method is possible thanks to the substitution of
every object for its representation in Natural
Language (NL). IE involves a transformation of a
collection of documents, generally helped by an IR
system. This collection of documents is transformed
into easier to assimilate and analyze information. IE
tries to extract relevant facts from documents,
whereas IR selects relevant documents. Therefore, it
might be said that IE works with a higher level of
granularity than IR. (Kosala, 2002). In our case, we
are applying IE techniques to a web portal. A web
portal consists of a collection of web pages, so the
method is completely applicable.
IR has been widely used for text classification
(Aronson et al., 1994; Liu et al., 2001) introducing
approaches such as Vector Space Model (VSM), K
nearest neighbour method (KNN), Bayesian
classification model, neural networks and Support
Vector Machine (SVM) (Lu et al., 2002). VSM is
the most frequently used model. In VSM, a
document is conceptually represented by a vector of
keywords extracted from the document, with
associated weights representing the importance of
these keywords in the document. Typically, the so-
called TF-IDF method is used for determining the
weight of a term (Lee et al., 1997). Term Frequency
(TF) is the frequency of occurrence of a term in a
document and Inverse Document Frequency (IDF)
varies inversely with the number of documents to
which the term is assigned (Salton & Buckley,
1988).
Although TF-IDF method for Term Weighting
(TW) has worked reasonably well for IR and has
been a starting point for more recent algorithms,
(Lee et al., 1997; Salton & Buckley, 1988; Liu et al.,
2001; Zhao & Karypis, 2002; Lertnattee &
Theeramunkong, 2002; Xu et al., 2003), it was never
taken into account that some other aspects of
keywords may be important for determining term
130
Ropero J., Gómez A., León C. and Carrasco A. (2009).
TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD VS. CLASSICAL TF-IDF METHOD FOR WEB INFORMATION EXTRACTION.
In Proceedings of the 11th International Conference on Enterprise Information Systems - Artificial Intelligence and Decision Support Systems, pages
130-137
DOI: 10.5220/0001982901300137
Copyright
c
 SciTePress

weights apart from TF and IDF: first of all, we
should consider the degree of identification of an
object if only the considered keyword is used. This
parameter has a strong influence on the final value
of a term weight if the degree of identification is
high. The more a keyword identifies an object, the
higher value for the corresponding term weight;
secondly, we should also consider the existance of
join terms.
In this paper, we introduce a fuzzy logic (FL)
based term weighting scheme. This scheme bears in
mind all these features for calculating the weight of
a term, taking advantage of fuzzy logic flexibility.
Fuzzy logic makes it possible to have a non-rigid
term weighting.
2 METHODOLOGY FOR IE
As said above, we are applying IE to a web portal.
Particularly, we have worked with the University of
Seville web portal. To carry out Information
Extraction, it is necessary to identify web page and
object, that is to say, every web page in a portal is
considered an object. These objects are gathered in a
hierarchical structure. An object is classified under a
unique criterion - or group of criteria -.
In our case, we have taken advantage of the
hierarchical structure of a web page to divide the
portal in three levels: Topic, Section and Object. The
size of every level is variable. Every object is
represented by means of a set of questions
formulated in NL. We have called these questions
standard questions. The number of standard
questions associated to every web page is variable,
depending on the amount of information contained
in every page, its importance and the number of
synonymous of index terms. Logically, System
Administrator’s knowledge about the jargon of the
related field is pretty important. The higher his
knowledge, the higher reliability of the proposed
standard questions, as they shall be more similar to
possible user consultations. After all, users are the
ones who try to extract the information. Our study
was based both on the study of the web pages
themselves and on previous consultations -
University of Seville bank of questions - .
Once standard questions are defined, index terms
are extracted from them. We have defined these
index terms as words, though they also may be
compound terms. Index terms are the ones that better
represent a standard question. Every index term is
associated with its correspondent term weight. This
weight has a value between 0 and 1 and depends on
the importance of the term in every hierarchic level.
The higher importance in a level, the higher is the
term weight. In addition, term weight is not constant
for every level, as the importance of a word to
distinguish a topic from the others may be very
different from its importance to distinguish between
two objects.
An example of the followed methodology is
shown in Table 1.
Table 1: Example of the followed methodology.
STEP EXAMPLE
Step 1: Web page
identified by standard/s
question/s
- Web page:
www.us.es/univirtual/internet
- Standard question : Which
services can I access as a virtual
user at the University of Seville?
Step 2: Locate
standard/s question/s in
the hierarchical
structure.
Topic 12: Virtual University
Section 6: Virtual User
Object 2.
Step 3: Extract index
terms
Index terms: ‘services’, ‘virtual’,
‘user’
Step 4: Term weighting See section 4
When a user consultation is made, these term
weights are the inputs to a fuzzy logic system, which
must detect the object to which the correspondent
user consultation refers. System operation is
described in (Ropero et al., 2007).
3 TERM WEIGHTING
As said in previous sections of this paper, there are a
few weights associated with every index term. The
values of the weights must be related somehow to
the importance of an index term in its corresponding
set of knowledge - in our case, Topic, Section or
Object -. We may consider two options to define
these weights:
An expert in the matter should evaluate
intuitively the importance of the index terms. This
method is simple, but it has the disadvantage of
depending exclusively on the knowledge engineer. It
is very subjective and it is not possible to automate
the method.
The generation of automated weights by means
of a set of rules. The most widely used method for
TW is the TF-IDF method, but we propose a novel
Fuzzy Logic based method, which achieves better
results in IE.
TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD VS. CLASSICAL TF-IDF METHOD FOR WEB
INFORMATION EXTRACTION
131

3.1 The TF-IDF Method
The idea of automatic text retrieval systems based
on the identification of text content and associated
identifiers is dated in the 50s, but it was Gerard
Salton in the late 70s and the 80s who laid the
foundations of the existing relation between these
identifiers and the texts they represent (Salton &
Buckley, 1988). Salton suggested that every
document D could be represented by term vectors tk
and a set of weights wdk, which represent the weight
of the term tk in document D, that is to say, its
importance in the document.
A TW system should improve efficiency in terms
of two main factors, recall and precision. Recall
bears in mind the fact that the most relevant objects
for the user must be retrieved. Precision takes into
account that strange objects must be rejected. (Ruiz
& Srinivasan, 1998). Recall may be defined as the
number of retrieved relevant objects divided by the
total number of objects. On the other hand, precision
is the number of retrieved relevant objects divided
by the total number of retrieved objects. Recall
improves if high-frequency terms are used, as such
terms will make it possible to retrieve many objects,
including the relevant ones. Precision improves if
low-frequency terms are used, as specific terms will
isolate the relevant objects from the non-relevant
ones. In practice, compromise solutions are used,
using terms which are frequent enough to reach a
reasonable level of recall without producing a too
low precision.
Therefore, terms that are mentioned often in
individual objects, seem to be useful to improve
recall. This suggests the utilization of a factor named
Term Frequency (TF). Term Frequency (TF) is the
frequency of occurrence of a term. On the other side,
another factor should favor the terms concentrated in
a few documents of the collection. The inverse
frequency of document (IDF) varies inversely with
the number of objects (n) to which the term is
assigned in an N-object collection. A typical IDF
factor is log (N/n). (Salton & Buckley, 1988). A
usual formula to describe the weight of a term j in
document i is:
w
i
j
= tf
i
j
x idf
j
. (1)
This formula has been modified and improved by
many authors to achieve better results in IR and IE
(Lee et al., 1997; Liu et al., 2001; Zhao & Karypis,
2002; Lertnattee & Theeramunkong, 2002; Xu et al.,
2003).
3.2 The FL based Method
The TF-IDF method works reasonably well, but it
has the disadvantage of not considering two key
aspects for us:
The degree of identification of the object if only
the considered index term is used. This parameter
has a strong influence on the final value of a term
weight if the degree of identification is high. The
more a keyword identifies an object, the higher
value for the corresponding term weight.
Nevertheless, this parameter creates two
disadvantages in terms of practical aspects when it
comes to carrying out a term weight automated and
systematic assignment. On the one hand, the degree
of identification is not deductible from any
characteristic of a keyword, so it must be specified
by the System Administrator. On the second hand,
the same keyword may have a different relationship
with every object.
The second parameter is related to join terms. In
the index term ‘term weighting’, this expression
would constitute a join term. Every single term in a
join term has a lower value than it would have if it
did not belong to it. However, if we combine all the
single terms in a join term, term weight must be
higher. A join term may really determine an object
whereas the appearance of only one of its single
terms may refer to another object.
The consideration of these two parameters
together with classical TF and IDF determines the
weight of an index term for every subset in every
level. The FL based method gives a solution to all
the problems and also gives two main advantages.
The solution to both problems is to create a table
with all the keywords and their corresponding
weights for every object. This table will be created
in the phase of keyword extraction from standard
questions. Imprecision practically does not affect the
working method due to the fact that both term
weighting and information extraction are based on
fuzzy logic, what minimizes possible variations of
the assigned weights. The way of extracting
information also helps to successfully overcome this
imprecision. In addition, the FL based method also
gives important advantages: on the one hand, term
weighting is automated; on the other hand, the level
of required expertise for an operator is lower. This
operator would not need to know anything about the
FL engine functioning, but only how many times
does a term appear in any subset and the answer to
these questions: a) Does a keyword undoubtedly
ICEIS 2009 - International Conference on Enterprise Information Systems
132

define an object by itself? b) Is a keyword tied to
another one?
In our case, the application of this method to a
web portal, the web portal developer himself may
define simultaneously the standard questions and
index terms associated with the object - a web page -
and the response to the questions mentioned above.
4 METHOD IMPLEMENTATION
This section shows how the TF-IDF method and the
FL based method were implemented in practise, in
order to compare both methods applying them to the
University of Seville web portal.
4.1 TF-IDF Method Implementation
As mentioned in previous sections, a reasonable
measure of the importance of a term may be
obtained by means of the TF-IDF product. However,
this formula has been modified and improved by
many authors to achieve better results in IR and IE.
Eventually, the chosen formula for our tests was the
one proposed by Liu et al. (Liu et al., 2001).
∑
=
+×
+×
=
m
k
kik
kik
ik
nNtf
nNtf
W
1
2
))01.0/log(
)01.0/log(
(2)
Where tfik is the ith term frequency of
occurrence in the kth subset - Topic / Section /
Object -. nk is the number of subsets to which the
termTk is assigned in a collection of N objects.
Consequently, it is taken into account that a term
might be present in other sets of the collection.
As an example, we are using the term ‘virtual’,
above used in the example in Section 2.
At Topic level:
- ‘Virtual’ appears 8 times in Topic 12 (tf
ik
= 8,
K=12).
- ‘Virtual’ appears twice in other Topics (n
k
= 3)
- There are 12 Topics in total (N=12) - for
normalizing, it is only necessary to know the
other tf
ik
and n
k
for the Topic -.
- Substituting, W
ik
= 0.20.
At Section level:
- ‘Virtual’ appears 3 times in Section 12.6 (tf
ik
=
3, K= 6)
- ‘Virtual’ appears 5 times in other Sections in
Topic 12 (n
k
= 6)
- There are 6 Sections in Topic 12 (N=6).
- Substituting, W
ik
= 0.17.
At Object level:
- ‘Virtual’ appears once in Object 12.6.2 (tf
ik
= 1,
K = 2). – Logically a term can only appear once
in an Object -.
- ‘Virtual’ appears twice in other Topics (n
k
= 3)
- There are 3 Objects in Section 12.6 (N=3).
- Substituting, W
ik
= 0.01. In fact, ‘virtual’
appears in all the Objects in Section 12.6, so it is
irrelevant to distinguish the Object.
Consequently, ‘virtual’ will be relevant to find
out that the Object is in Topic 12, Section 6, but
irrelevant to find out the definite Object, which
should be found according to other terms in a user
consultation.
4.2 FL based Method Implementation
As said in section 3.2, TF-IDF has the disadvantage
of not considering the degree of identification of the
object if only the considered index term is used and
the existance of tied keywords. Like TF-IDF
method, it is neccesary to know TF and IDF, and
also the answer to the questions mentioned in
section 3.2. FL based Term Weighting method is
defined below. Four questions must be answered to
determine the Term Weight of an Index Term:
- Question 1 (Q1): How often does an index term
appear in other subsets? - Related to IDF -.
- Question 2 (Q2): How often does an index term
appear in its own subset? - Related to TF -.
- Question 3 (Q3): Does an index term
undoubtedly define an object by itself?
- Question 4 (Q4): Is an index term tied to
another one?
Question 1
Term weight is partly associated to the question
‘How often does an index term appear in other
subsets?’. It is given by a value between 0 – if it
appears many times – and 1 - if it does not appear in
any other subset -. To define weights, we are
considering the times that the most used terms in the
whole set of knowledge appear.
Provided that there are 1114 index terms defined
in our case, we have assumed that 1 % of these
words must mark the border for the value 0 (11
words). As the eleventh most used word appears 12
times, whenever an index term appears more than 12
times in other subsets, we will give it the value of 0.
Values for every Topic are defined in Table 2.
TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD VS. CLASSICAL TF-IDF METHOD FOR WEB
INFORMATION EXTRACTION
133

Table 2: Term weight values for every Topic for Q1.
Times
appearing
0 1 2 3 4 5 6 7 8 9 10 11 12 >12
Value 1 0.9 0.8 0.7 0.64 0.59 0.53 0.47 0.41 0.36 0.3 0.2 0.1 0
Table 3: Term weight values for every Section for Q1.
Times appearing 0 1 2 3 4 5 > 5
Value 1 0.7 0.6 0.5 0.4 0.3 0
Table 4: Term weight values for every Object for Q1.
Times appearing 0 1 2 >2
Value 1 0.7 0.3 0
Table 5: Term weight values for every Topic and Section for Q2.
Times appearing 1 2 3 4 5 > 5
Value 0 0.3 0.45 0.6 0.7 1
Table 6: Term weight values for Q3.
Answer to Q3: Does a term define undoubtedly
a standard question?
Yes Rather No
Value 1 0.5 0
Between 0 and 3 times appearing -
approximately a third of the possible values - , we
consider that an index term belongs to the so called
HIGH set. Therefore, it is defined in its
correspondant fuzzy set with uniformly distributed
values between 0.7 and 1, as may be seen in Figure
1. Analogously, we may distribute all values
uniformly according to different fuzzy sets. Fuzzy
sets are triangular, on one hand for simplicity and on
the other hand because we tested other more
complex types of sets (Gauss, Pi type, etc) and the
results did not improve at all.
Figure 1: Input fuzzy sets.
Provided that different weights are defined in
every hierarchic level, we should consider other
scales to calculate them. As for the Topic Level we
were considering the immediately top level – the
whole set of knowledge - , for the Section level we
should consider the times that an index term appears
in a certain Topic. We again consider that 1 % of
these words must mark the border for the value 0 -
11 words - . The eleventh most used index term in a
unique Topic appears 5 times, so whenever a term
appears more than 5 times in other subsets, its
weight takes the value 0 at the Section of level.
Possible term weights for the level of Section are
shown in Table 3. The method is analogous and
considers the definition of the fuzzy sets. At the
level of Object, term weights are shown in Table 4.
Question 2
To find out the term weight associated to question 2
- Q2, How often does an index term appear in its
own subset? -, the reasoning is analogous. However,
we have to bear in mind that it is necessary to
consider the frequency inside a unique set of
knowledge, thus the number of appearances of index
terms decreases considerably. The list of the most
used index terms in a Topic must be considered
again. It also must be born in mind that the more an
index term appears in a Topic or Section, the higher
value for an index term. Q2 is senseless at the level
of Object. The proposed values are given in Table 5.
ICEIS 2009 - International Conference on Enterprise Information Systems
134

Table 7: Term weight values for Q4.
Number of index terms tied to
another index term
0 1 2 > 2
Value 1 0.7 0.3 0
Table 8: Rule definition for Topic and Section levels.
Rule number Rule definition Output
R1 IF Q1 = HIGH and Q2 ≠ LOW At least MEDIUM-HIGH
R2 IF Q1 = MEDIUM and Q2 = HIGH At least MEDIUM-HIGH
R3 IF Q1 = HIGH and Q2 = LOW Depends on other Questions
R4 IF Q1 = HIGH and Q2 = LOW Depends on other Questions
R5 IF Q3 = HIGH At least MEDIUM-HIGH
R6 IF Q4 = LOW Descends a level
R7 IF Q4 = MEDIUM If the Output is MEDIUM-
LOW, it descends to LOW
R8 IF (R1 and R2) or (R1 and R5) or (R2 and R5) HIGH
R9 In any other case MEDIUM-LOW
Question 3
In the case of question 3 – Q3, Does a term define
undoubtedly a standard question? - , the answer is
completely subjective and we propose the answers
‘Yes’, ‘Rather’ and ‘No’. Term weight values for
this question are shown in Table 6.
Question 4
Finally, question 4 – Q4, Is an index term tied to
another one? – deals with the number of index terms
tied to another one. We propose term weight values
for this question in Table 7. Again, the values 0.7
and 0.3 are a consequence of considering the border
between fuzzy sets – see Figure 1-.
After considering all these factors, fuzzy rules
for Topic and Section levels are defined in Table 8.
This rules cover all the 81 possible combinations.
Note that, apart from the three input sets mentioned
in previous sections, four output sets have been
defined - HIGH, MEDIUM-HIGH, MEDIUM-LOW
and LOW-, as may be seen in Figure 2. At the level
of Object, we must discard question 2 and rules
change.
The only aspect which has not been defined yet
is about multiple appearances in a Topic or Section.
I.e., it is possible that the answer to question 3 is
‘Rather’ in one case ‘No’ in another one. In this
case, a weighted average of the corresponding term
weights is calculated.
An example of all the process is shown below
Example
Object 12.6.2 is defined by the following standard
question :
Which services can I access as a virtual user
at the University of Seville?
If we consider the term ‘virtual’:
- At Topic level:
- ‘Virtual’ appears twice in other Topics in the
whole set of knowledge, so that the value associated
to Q1 is 0.80.
Figure 2: Output fuzzy sets.
- ‘Virtual’ appears 8 times in Topic 12, so that
the value associated to Q2 is 1.
- The response to Q3 is ‘Rather’ in 5 of the 8
times and ‘No’ in the other three, so that the value
associated to Q3 is a weighted average: (5*0.5 +
3*0)/8 = 0.375.
- Term ‘virtual’ is tied to one term 7 times and it
is tied to two terms once. Therefore, the average is
TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD VS. CLASSICAL TF-IDF METHOD FOR WEB
INFORMATION EXTRACTION
135

Table 9: Test comparison between both methods.
Cat1 Cat2 Cat3 Cat4 Cat5 Total
TF-IDF
Method
466
(50.98%)
223
(24.40%)
53
(5.80%)
79
(8.64%)
93
(10.18%)
914
FL Method 710
(77.68%)
108
(11.82%)
27
(2.95%)
28
(3.06%)
41
(4.49%)
914
1.14 terms. A linear extrapolation leads to a value
associated to Q4 of 0.65.
- With all the values as inputs for the fuzzy
logic engine, we obtain a term weight of 0.53.
- At Section level
- ‘Virtual’ appears 5 times in other Sections
corresponding to Topic 12, so that the value
associated to Q1 is 0.30.
- ‘Virtual’ appears 3 times in Topic 12, so that
the value associated to Q2 is 0.45.
- The response to Q3 is ‘Rather’ in all cases, so
that the value associated to Q3 is 0.5.
- Term ‘virtual’ is tied to term ‘user’ so that the
value associated to Q4 is 0.7.
- With all the values as inputs for the fuzzy
logic engine, we obtain a term weight of 0.45.
- At Object level:
- ‘Virtual’ appears twice in other Objects
corresponding to Section 12.6, so that the value
associated to Q1 is 0.30.
- The response to Q3 is ‘Rather’, so that the
value associated to Q3 is 0.5.
- Term ‘virtual’ is tied to term ‘user’ so that the
value associated to Q4 is 0.7.
- With all the values as inputs for the fuzzy
logic engine, we obtain a term weight of 0.52. We
can see the difference with the corresponding term
weight obtained with the TF-IDF method, but it is
exactly what we are looking for: not only the
desired object must be retrieved, but the most
closely related to it.
5 TESTS AND RESULTS
Tests have been done on the University of Seville
web portal. This web portal has 50,000 daily visits,
what qualifies it into the 10% most visited
University portals – there are more than 4,000 -.
As there is much information in it, 253 objects
grouped in 12 Topics were defined. All these
groups were made up of a variable number of
Sections and Objects. 2107 standard questions
surged from these 253 Objects, but slightly more
than the half of them were eliminated for these
tests because of being very similar to others.
Eventually, tests consisted of 914 user
consultations.
To compare results, we considered the position
in which the correct answer appeared among the
retrieved answers, according to fuzzy engine
outputs. The first necessary step to follow is to
define the overcoming thresholds for the fuzzy
engine. This way, Topics and Sections that are not
related with the Object to identify are eliminated.
We also have to define low enough thresholds, in
order to be able to obtain also related Objects. We
suggest to present between 1 and 5 answers,
depending on the number of related Objects.
The results of the consultation were sorted in 5
categories:
- Category Cat1: the correct answer is retrieved
as the only answer or it is the one that has a higher
degree of certainty between the answers retrieved
by the system.
- Category Cat2: The correct answer is
retrieved between the 3 with higher degree of
certainty -excluding the previous case -.
- Category Cat3: The correct answer is
retrieved between the 5 with higher degree of
certainty - excluding the previous cases -.
- Category Cat4: The correct answer is
retrieved, but not between the 5 with higher degree
of certainty.
- Category Cat5: The correct answer is not
retrieved by system.
The ideal situation comes when the desired
Object is retrieved as Cat1, though Cat2 and Cat3
would be reasonably acceptable. The obtained
results are shown in Table 9. Though the obtained
results with the TF-IDF method are quite
reasonable, 81.18 % of the objects being retrieved
between the first 5 options - and more than as
Cat1, the FL based method turns out to be clearly
better, with 92.45 % of the desired Objects
retrieved - and more than three quarters as the first
option -.
ICEIS 2009 - International Conference on Enterprise Information Systems
136

6 CONCLUSIONS
A FL based Term Weighting method has been
presented as an alternative to classical TF-IDF
method.
The main advantage for the proposed method is
the obtention of better results, especially in terms
of extracting not only the most suitable
information but also related information.
This method will be used for the design of a
Web Intelligent Agent which will soon start to
work for the University of Seville web page.
REFERENCES
Aronson, A.R, Rindflesch, T.C, Browne, A. C., 1994.
Exploiting a large thesaurus for information
retrieval. Proceedings of RIAO, pp. 197-216.
Kosala, R., Blockeel, H., 2002. Web Mining Research: A
Survey. SIGKDD Explorations: Newsletter of the
Special Interest Group (SIG) on Knowledge
Discovery & Data Mining, ACM, Vol. 2 (2000).
Kwok, K. L., 1989. A neural network for probabilistic
information retrieval. Proceedings of the 12th annual
international ACM SIGIR conference on Research
and development in information retrieval.
Cambridge, Massachusetts, United States.
Lee, D.L., Chuang, H., Seamons, K., 1997. Document
ranking and the vector-space model. IEEE Software,
Vol. 14, Issue 2, p. 67 – 75.
Lertnattee, V., Theeramunkong, T. 2003. Combining
homogenous classifiers for centroid-based text
classification. Proceedings of the 7
th
International
Symposium on Computers and Communications, pp.
1034-1039.
Liu, S., Dong, M., Zhang, H., Li, R. Shi, Z., 2001. An
approach of multi-hierarchy text classification
Proceedings of the International Conferences on Info-
tech and Info-net, 2001. Beijing. Vol 3, pp. 95 – 100.
Lu, M., Hu, K., Wu, Y., Lu, Y., Zhou, L., 2002.
SECTCS: towards improving VSM and Naive
Bayesian classifier. IEEE International Conference
on Systems, Man and Cybernetics, Vol. 5, p. 5.
Ropero J., Gomez, A., Leon, C., Carrasco, A. 2007.
Information Extraction in a Set of Knowledge Using
a Fuzzy Logic Based Intelligent Agent. Lecture
Notes in Computer Science. Vol. 4707, pp. 811-820.
Ruiz, M.E., Srinivasan, P., 1998. Automatic Text
Categorization Using Neural Networks. Advances in
Classification Research vol. 8: Proceedings of the 8th
ASIS SIG/CR Classification Research Workshop. Ed.
Efthimis Efthimiadis. Information Today,
Medford:New Jersey. 1998. pp 59-72.
Salton, G., Buckley, C., 1996. Term Weighting
Approaches in Automatic Text Retrieval. Information
Processing and Management, Vol.32 (4), pp. 431-
443.
Xu, J., Wang, Z. 2003. TCBLSA: A new method of text
clustering. International Conference on Machine
Learning and Cybernetics. Vol. 1, pp. 63-66.
Zhao, Y., Karypis, G., 2002. Improving precategorized
collection retrieval by using supervised term
weighting schemes. Proceedings of the International
Conference on Information Technology: Coding and
Computing, 2002. pp 16 – 21.
TERM WEIGHTING: NOVEL FUZZY LOGIC BASED METHOD VS. CLASSICAL TF-IDF METHOD FOR WEB
INFORMATION EXTRACTION
137