文章基本信息

标题：Stable Classification of Text Genres
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作者：Philipp Petrenz ; Bonnie Webber
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2011
卷号：37
期号：2
页码：385-393
DOI：10.1162/COLI_a_00052
语种：English
出版社：MIT Press
摘要：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.