Paper: Automatically Generating Annotator Rationales to Improve Sentiment Classification

ACL ID P10-2062
Title Automatically Generating Annotator Rationales to Improve Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

Oneofthecentralchallengesinsentiment- based text categorization is that not ev- ery portion of a document is equally in- formative for inferring the overall senti- ment of the document. Previous research has shown that enriching the sentiment la- bels with human annotators’ “rationales” can produce substantial improvements in categorization performance (Zaidan et al., 2007). We explore methods to auto- matically generate annotatorrationales for document-level sentiment classification. Rather unexpectedly, we find the automat- ically generated rationales just as helpful as human rationales.