Modeling Perspective Using Adaptor Grammars
Published 9 October 2010
Eric A. Hardisty, Jordan Boyd‐Graber, Philip Resnik
Citations40
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TL;DR
It is demonstrated that an adaptive naive Bayes model captures multiword lexical usages associated with perspective, and a new state-of-the-art for perspective classification results are established using the Bitter Lemons corpus, a collection of essays about mid-east issues from Israeli and Palestinian points of view.
Abstract
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 conservative rather than progressive viewpoint. However, going beyond unigram or bigram features in perspective classification gives rise to problems of data sparsity. We address this problem using nonparametric Bayesian modeling, specifically adaptor grammars (Johnson et al., 2006). We demonstrate that an adaptive naïve Bayes model captures multiword lexical usages associated with perspective, and establishes a new state-of-the-art for perspective classification results using the Bitter Lemons corpus, a collection of essays about mid-east issues from Israeli and Palestinian points of view. 1
Keywords
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