Paper: Identifying Sources Of Opinions With Conditional Random Fields And Extraction Patterns

ACL ID H05-1045
Title Identifying Sources Of Opinions With Conditional Random Fields And Extraction Patterns
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

Recent systems have been developed for sentiment classification, opinion recogni- tion, and opinion analysis (e.g. , detect- ing polarity and strength). We pursue an- other aspect of opinion analysis: identi- fying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Con- ditional Random Fields (Lafferty et al. , 2001) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source iden- tification as a sequence tagging task, Au- toSlog learns extraction patterns. Our re- sults show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79.3% precision and 59.5% recall using a head noun matching measure, and 81.2%...