Quarterly (March, June, September, December)
160 pp. per issue
6 3/4 x 10
ISSN
0891-2017
E-ISSN
1530-9312
2014 Impact factor:
1.23

Computational Linguistics

Paola Merlo, Editor
June 2011, Vol. 37, No. 2, Pages 385-393
(doi: 10.1162/COLI_a_00052)
© 2011 Association for Computational Linguistics
Stable Classification of Text Genres
Article PDF (208.18 KB)
Abstract

Every text has at least one topic and at least one genre. Evidence for a text's topic and genre comes, in part, from its lexical and syntactic features—features used in both Automatic Topic Classification and Automatic Genre Classification (AGC). Because an ideal AGC system should be stable in the face of changes in topic distribution, we assess five previously published AGC methods with respect to both performance on the same topic–genre distribution on which they were trained and stability of that performance across changes in topic–genre distribution. Our experiments lead us to conclude that (1) stability in the face of changing topical distributions should be added to the evaluation critera for new approaches to AGC, and (2) Part-of-Speech features should be considered individually when developing a high-performing, stable AGC system for a particular, possibly changing corpus.