The importance of stop word removal on recall values in text categorization

Author

Silva, Catarina ; Ribeiro, Bemardete

Author_Institution

Dept. de Engenharia Inf., Coimbra Univ., Portugal

Volume

3

fYear

2003

fDate

20-24 July 2003

Firstpage

1661

Abstract

Given a data set and a learning task such as classification, there are two prime motives for executing some kind of data set reduction. On one hand there is the possible algorithm performance improvement. On the other hand the decrease in the overall size of the data set can bring advantages in storage space used and time spent computing. Our purpose is to determine the importance of several basic reduction techniques on Support Vector Machines, by comparing their relative performance improvement when applied on the standard REUTERS-21578 benchmark.

Keywords

classification; data reduction; indexing; information retrieval; support vector machines; text editing; REUTERS-21578 benchmark; algorithm performance improvement; data set reduction; learning task; stop word removal; storage space; support vector machines; text categorization; text classification; Humans; Information retrieval; Internet; Large-scale systems; Support vector machine classification; Support vector machines; Taxonomy; Text categorization; Text mining; Text processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223656

Filename

1223656