A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms

被引：0

作者：

Athanasios Kehagias

Vassilios Petridis

Vassilis G. Kaburlasos

Pavlina Fragkou

机构：

[1] Aristotle University of Thessaloniki (AUTh),Department of Math., Phys. and Comp. Sciences, Division of Mathematics

[2] Aristotle University of Thessaloniki (AUTh),Department of Electrical and Computer Engineering, Division of Electronics and Computer Engineering

[3] Technological Educational Institute of Kavala,Department of Industrial Informatics, Division of Software Systems

来源：

Journal of Intelligent Information Systems | 2003年 / 21卷

关键词：

text categorization; word senses; information retrieval; FLNMAP with voting;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Most of the text categorization algorithms in the literature represent documents as collections of words. An alternative which has not been sufficiently explored is the use of word meanings, also known as senses. In this paper, using several algorithms, we compare the categorization accuracy of classifiers based on words to that of classifiers based on senses. The document collection on which this comparison takes place is a subset of the annotated Brown Corpus semantic concordance. A series of experiments indicates that the use of senses does not result in any significant categorization improvement.

引用

页码：227 / 247

页数：20

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