Multilingual document clustering, topic extraction and data transformations

Joaquim Francisco Ferreira Silva; João Tiago Praça Nunes Mexia; Carlos A. Coelho; José Gabriel Pereira Lopes

Multilingual document clustering, topic extraction and data transformations

Joaquim Francisco Ferreira Silva, João Tiago Praça Nunes Mexia, Carlos A. Coelho, José Gabriel Pereira Lopes

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This paper describes a statistics-based approach for clustering documents and for extracting cluster topics. Relevant Expressions (REs) are extracted from corpora and used as clustering base features. These features are transformed and then by using an approach based on Principal Components Analysis, a small set of document classification features is obtained. The best number of clusters is found by ModelBased Clustering Analysis. Data transformations to approximate to normal distribution are done and results are discussed. The most important REs are extracted from each cluster and taken as cluster topics.

Original language	English
Title of host publication	Progress in Artificial Intelligence
Editors	P. Brazdil, A. Jorge
Place of Publication	Berlin
Publisher	Springer
Pages	74-87
Number of pages	14
ISBN (Print)	3-540-43030-X
Publication status	Published - 1 Jan 2001

Keywords

Document Clustering
Model-based clustering
Clustering documents
Data transformation
Document Classification

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abstract = "This paper describes a statistics-based approach for clustering documents and for extracting cluster topics. Relevant Expressions (REs) are extracted from corpora and used as clustering base features. These features are transformed and then by using an approach based on Principal Components Analysis, a small set of document classification features is obtained. The best number of clusters is found by ModelBased Clustering Analysis. Data transformations to approximate to normal distribution are done and results are discussed. The most important REs are extracted from each cluster and taken as cluster topics.",

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AU - Silva, Joaquim Francisco Ferreira

AU - Mexia, João Tiago Praça Nunes

AU - Coelho, Carlos A.

AU - Lopes, José Gabriel Pereira

PY - 2001/1/1

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N2 - This paper describes a statistics-based approach for clustering documents and for extracting cluster topics. Relevant Expressions (REs) are extracted from corpora and used as clustering base features. These features are transformed and then by using an approach based on Principal Components Analysis, a small set of document classification features is obtained. The best number of clusters is found by ModelBased Clustering Analysis. Data transformations to approximate to normal distribution are done and results are discussed. The most important REs are extracted from each cluster and taken as cluster topics.

AB - This paper describes a statistics-based approach for clustering documents and for extracting cluster topics. Relevant Expressions (REs) are extracted from corpora and used as clustering base features. These features are transformed and then by using an approach based on Principal Components Analysis, a small set of document classification features is obtained. The best number of clusters is found by ModelBased Clustering Analysis. Data transformations to approximate to normal distribution are done and results are discussed. The most important REs are extracted from each cluster and taken as cluster topics.

KW - Document Clustering

KW - Model-based clustering

KW - Clustering documents

KW - Data transformation

KW - Document Classification

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-23044529225&origin=resultslist&sort=plf-f&src=s&st1

M3 - Chapter

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EP - 87

BT - Progress in Artificial Intelligence

A2 - Brazdil, P.

A2 - Jorge, A.

PB - Springer

CY - Berlin

ER -

Multilingual document clustering, topic extraction and data transformations

Abstract

Keywords

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