Accurate Stemming of Dutch for Text Classification

Tanja Gaustad; Gosse Bouma

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Accurate Stemming of Dutch for Text Classification

于Computational Linguistics in the Netherlands 2001

著者：

Tanja Gaustad

Tanja Gaustad
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Gosse Bouma

Gosse Bouma
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类型:: 章节

Page Range:: 104–117

DOI:: https://doi.org/10.1163/9789004334038_010

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Abstract

This paper investigates the use of stemming for classification of Dutch (email) texts. We introduce a stemmer, which combines dictionary lookup (implemented efficiently as a finite state automaton) with a rule-based backup strategy and show that it outperforms the Dutch Porter stemmer in terms of accuracy, while not being substantially slower.

For text classification, the most important property of a stemmer is the number of words it (correctly) reduces to the same stem. Here the dictionary-based system also outperforms Porter. However, evaluation of a Bayesian text classification system with either no stemming or the Porter or dictionary-based stemmer on an email classification and a newspaper topic classification task does not lead to significant differences in accuracy. We conclude with an analysis of why this is the case.

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Computational Linguistics in the Netherlands 2001

Selected Papers from the Twelfth CLIN Meeting

丛编： Language and Computers, 卷： 45

Cover Computational Linguistics in the Netherlands 2001

ISBN:: 9789004334038

出版社:: Brill

印刷出版日期:: 01 Jan 2002

Subjects
- Languages and Linguistics
  - Applied Linguistics

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Accurate Stemming of Dutch for Text Classification

Abstract

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Computational Linguistics in the Netherlands 2001

Selected Papers from the Twelfth CLIN Meeting

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