Improving Text Categorization Bootstrapping via Unsupervised Learning

Presenter : Bo-Sheng Wang 　Authors :ALFIO GLIOZZO, IDO DAGAN

TSLP, 2009

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Outlines

• Motivation• Objectives• Methodology• Evaluation• Experiments• Conclusions• Comments

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Motivation

• Supervised systems for text categorization require large amounts of hand-labeled texts

• IL inherently suffers from a score scaling problem and very little information about the intension of a category.

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Objectives

• Investigate and improve two specific weaknesses that inherently affect the IL schema.

Latent Semantic Index

Gaussian Mixture Algorithm

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Methodology-Latent Semantic Index

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Vector Semantic Model

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Methodology-Latent Semantic Index

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Methodology-Latent Semantic Index

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Methodology-Gaussian Mixture Algorithm

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• This paper propose mapping the similarity values into class posterior probabilities using unsupervised estimation of Gaussian mixtures.

Methodology-Gaussian Mixture Algorithm

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Seeds

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Evaluation-Impact of LSI Similarity and GM on IL Performance

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Evaluation-Extensional vs. Intensional Learning

• A major of a comparison between IL and EL is the amount of supervision required to obtain level of performance.

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Experiments –

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Conclusions

• We obtained competitive performance using only the category names as initial seeds.

• Drastically reduce the number of seeds while significantly improving the performance.

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Comments

• Advantages– Performance,

• Disadvantage– Time

• Applications– Text Mining

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