Fuzzy Combinations of Criteria: An Application to Web Page Representation for Clustering - Cicling12

Fuzzy Combinations of Criteria: An Application toWeb Page Representation for Clustering

Alberto Perez Garcıa-Plaza, Vıctor Fresno, Raquel Martınez

NLP & IR Group, Distance Learning University (UNED)

CICLing 2012, New Delhi, India

March 15, 2012

Motivation Understanding the system Improving the Combination Summary

Table of Contents

1 MotivationWeb Page RepresentationLinear Combination of CriteriaNonlinear Combination of Criteria

2 Understanding the systemExperimental SettingsDimension Reduction AnalysisStudy of Individual Criteria

3 Improving the Combination

4 Summary

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Fuzzy Combinations of Criteria: An Application to Web Page Representation for Clustering

Motivation

Main goal

To understand how to represent web pages for clustering.

Question

How to combine different page features to represent web pages?

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Motivation

Main goal

To understand how to represent web pages for clustering.

Question

How to combine different page features to represent web pages?

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Table of Contents

4 Summary

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Web Page Representation

Hypothesis

A good document representation should be based on how humansread documents.

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Different Criteria for Web PageRepresentation

Criteria:

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Criteria:�� Title

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Criteria:�� Title

�� Emphasis

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Word positions:

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Word positions:�� Preferential

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Word positions:�� Preferential

�� Standard

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Table of Contents

4 Summary

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Linear Combination of Criteria

For example: Analytical Combination of Criteria (acc)1.Importance of a term in a document:

Ik = tk it + ek ie + fk if + pk ip (1)

Ik = 1 ∗ 0.4 + 0.6 ∗ 0.3 + 0 ∗ 0.2 + 0 ∗ 0.1 = 0.4 (2)

Drawback

The importance of a term in a component is calculated regardlessthe rest of the components.

1V. Fresno and A. Ribeiro. An analytical approach to concept extraction in html environments. J. Intell. Inf.

Syst., 22(3):215–235, 2004.

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Linear Combination of Criteria

For example: Analytical Combination of Criteria (acc)1.Importance of a term in a document:

Ik = tk it + ek ie + fk if + pk ip (1)

Ik = 1 ∗ 0.4 + 0.6 ∗ 0.3 + 0 ∗ 0.2 + 0 ∗ 0.1 = 0.4 (2)

Drawback

The importance of a term in a component is calculated regardlessthe rest of the components.

1V. Fresno and A. Ribeiro. An analytical approach to concept extraction in html environments. J. Intell. Inf.

Syst., 22(3):215–235, 2004.

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Example: acc

Call to Arms

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Example: accExample of rethoric title

“Call to arms” is the title of apage that contains an articleabout the new trades made byNew York Yankees baseball teamand how these trades affect toBoston Red Sox, their main rivalin the Major League Baseball.

Drawback

Title terms are not related todocument topic.

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Table of Contents

4 Summary

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Nonlinear Combination of Criteria

Fuzzy Combination of Criteria (fcc)2 allows nonlinearcombinations of criteria.

It is possible to define related conditions.

It produces vectors within the VSM.

2A. Ribeiro, V. Fresno, M. C. Garcia-Alegre, and D. Guinea. A fuzzy system for the web page representation.

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Example: fcc

Example of rethoric title

Now, we can express that a termshould appear in the title andemphasized to be consideredimportant.

Nonlinearity

Title terms can be considered notimportant because they do notappear in the rest of the text.

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Example: fcc

Example of rethoric title

Now, we can express that a termshould appear in the title andemphasized to be consideredimportant.

Nonlinearity

Title terms can be considered notimportant because they do notappear in the rest of the text.

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A quick glance at fcc

Close to natural language.

Knowledge base: defined by a set of IF-THEN rules.

Rules are based on how humans read documents.

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Table of Contents

4 Summary

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Table of Contents

4 Summary

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Basic Clustering Settings

We remove stopwords, punctuation and suffixes (Porter’salgorithm).

Clustering: Cluto-rbr with default parameters.

Web page representations: tf-idf and fcc

Dimension reduction techniques (100, 500, 1000, 2000 and5000 features): mft and lsi.

Banksearch and Webkb.

F-measure to evaluate clustering quality.

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Table of Contents

4 Summary

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Dimension Reduction Analysis

Hypothesis

If lsi improves mft, then the weighting function is not able to findthe most representative terms.

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Rep. Avg. S.D.Banksearchtf-idf mft 0,748 0,028tf-idf lsi 0,756 0,005fcc mft 0,756 0,019fcc lsi 0,769 0,011Webkbtf-idf mft 0,460 0,051tf-idf lsi 0,507 0,006fcc mft 0,469 0,009fcc lsi 0,466 0,011

Conclusion

The weighting function is not working as well as it could.

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Rep. Avg. S.D.Banksearchtf-idf mft 0,748 0,028tf-idf lsi 0,756 0,005fcc mft 0,756 0,019fcc lsi 0,769 0,011Webkbtf-idf mft 0,460 0,051tf-idf lsi 0,507 0,006fcc mft 0,469 0,009fcc lsi 0,466 0,011

Conclusion

Results for fcc in Webkb dataset are surprisingly bad.

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Table of Contents

4 Summary

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Results for Criteria Analysis

Rep.\Dim. 100 500 1000 2000 5000Banksearchfcc mft 0,723 0,757 0,768 0,765 0,768title 0,626 0,646 0,632 0,634 0,639emphasis 0,586 0,671 0,674 0,685 0,693frequency 0,689 0,715 0,720 0,724 0,731position 0,310 0,525 0,538 0,599 0,608

For Banksearch, fcc get always higher values than individualcriteria, so the combination works better in all cases.

Frequency seems to be the best among the individual criteria.

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Results for Criteria Analysis

Rep.\Dim. 100 500 1000 2000 5000Webkbfcc mft 0,453 0,472 0,475 0,468 0,475title 0,432 0,433 0,404 0,488 0,479emphasis 0,415 0,431 0,433 0,465 0,489frequency 0,441 0,460 0,460 0,468 0,446position 0,301 0,283 0,317 0,281 0,286

For Webkb, fcc does not always outperform the others.

Frequency is not always the best among the individual criteria.

When title and emphasis could lead to a better clustering, thecombination get worse.

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Table of Contents

4 Summary

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Improving the Combination

Frequency should influence the decision more than position.

IF Title AND Frequency AND Emphasis AND Position THEN ImportanceLow Medium Low Preferential ⇒ LowLow Medium Low Standard ⇒ No

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Extended Fuzzy Combination of Criteria (efcc)

IF Title AND Frequency AND Emphasis AND Position THEN ImportanceHigh High ⇒ Very HighHigh Medium Preferential ⇒ HighHigh Medium Standard ⇒ MediumHigh Low Preferential ⇒ MediumHigh Low Standard ⇒ LowLow High Preferential ⇒ HighLow High Standard ⇒ MediumLow Medium Preferential ⇒ MediumLow Medium Standard ⇒ LowLow Low Preferential ⇒ LowLow Low Standard ⇒ No

High ⇒ Very HighMedium ⇒ MediumLow ⇒ No

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System Comparison

With efcc, both reduction methods get similar results.

Rep. Avg. S.D.Banksearchtf-idf lsi 0,756 0,005fcc lsi 0,769 0,011efcc mft 0,760 0,014efcc lsi 0,758 0,013Webkbtf-idf lsi 0,507 0,006fcc mft 0,469 0,009efcc mft 0,532 0,032efcc lsi 0,483 0,000

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System Comparison

efcc solves the problems of fcc in Webkb.

Rep. Avg. S.D.Banksearchtf-idf lsi 0,756 0,005fcc lsi 0,769 0,011efcc mft 0,760 0,014efcc lsi 0,758 0,013Webkbtf-idf lsi 0,507 0,006fcc mft 0,469 0,009efcc mft 0,532 0,032efcc lsi 0,483 0,000

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Table of Contents

4 Summary

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Summary

We present a term weighting function based on how humanread documents.

The representation is not oriented to concrete sets of webpages.

Nonlinear systems help express relations among criteria.

With a good term weighting function it is possible to uselightweight dimension reduction techniques.

Our system try to ease the communication between technicaland linguistic experts.

Anchor texts were also studied as a way of adding contextualinformation.

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Summary

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Summary

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Summary

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Summary

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Summary

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Thank You!

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Fuzzy Combinations of Criteria: An Application to Web Page Representation for Clustering - Cicling12

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Transcript of Fuzzy Combinations of Criteria: An Application to Web Page Representation for Clustering - Cicling12