Paper: Modeling Perspective Using Adaptor Grammars

ACL ID D10-1028
Title Modeling Perspective Using Adaptor Grammars
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010

Strong indications of perspective can often come from collocations of arbitrary length; for example, someone writing get the government out of my X is typically expressing a conserva- tive rather than progressive viewpoint. How- ever, going beyond unigram or bigram features in perspective classification gives rise to prob- lems of data sparsity. We address this prob- lem using nonparametric Bayesian modeling, specifically adaptor grammars (Johnson et al., 2006). We demonstrate that an adaptive na¨ıve Bayes model captures multiword lexical usages associated with perspective, and establishes a new state-of-the-art for perspective classifica- tion results using the Bitter Lemons corpus, a collection of essays about mid-east issues from Israeli and Palestinian points of view.