Stable Classification of Text Genres

Philipp Petrenz, Bonnie Webber

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)385-393
Number of pages9
JournalComputational Linguistics
Volume37
Issue number2
DOIs
Publication statusPublished - Jun 2011

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