Intelligent Database Systems Lab

Presenter: CHANG, SHIH-JIE

Authors: Tao Liu, Zheng Chen, Benyu Zhang, Wei-ying Ma, Gongyi Wu

2004.ICDM.

Improving Text Classification using Local Latent Semantic Indexing

Intelligent Database Systems Lab

Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments

Intelligent Database Systems Lab

Motivation

• Global LSI ignores class discrimination. It has no help to improve the discrimination power of document classes, so it always yields no better on classification.

• In Local LSI, due to the weighting problem, the improvement of classification performance very limited.

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Objectives

• Propose new local LSI method(Local Relevancy Weighted LSI) to solve problem.

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Methodology - Local LSI • statistic (QS-CHI): measures the association between

the term and the topic.

• Mutual Information (QS-MI): measures how important a term to a topic.

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LRW-LSI Training (1) initial classifier IC of topic c is used to assign initial relevancy score ( rs ) to each training document. (2) each training document is weighted. (3) the top n documents are selected to generate the local term-by-document matrix of the topic c. (4) a truncated SVD is performed to generate the local semantic space. (5) all other weighted training documents are folded into

the new space. (6) all training documents in local LSI vector are used to train a real classifier RC of topic c .

Methodology-Local Relevancy Weighted LSI

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Methodology-Local Relevancy Weighted LSI

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Experiments

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Experiments

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Experiments

Intelligent Database Systems Lab

Experiments

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Experiments

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Conclusions• LRW-LSI can improve the classification performance

greatly using a much smaller dimension compared to the global LSI and local LSI methods.

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Comments• Advantages

- LRW-LSI is quite effective.

• Applications- Text Classification.