Hybrid Models for Lexical Acquisition of Correlated Styles
Julian Brooke, Graeme Hirst · 2013
Automated lexicon acquisition from cor-pora represents one way that large datasets can be leveraged to provide resources for a variety of NLP tasks. Our work applies techniques popularized in sentiment lexi-con acquisition and topic modeling to the broader task of creating a stylistic lexicon. A novel aspect of our approach is a fo-cus on multiple related styles, first extract-ing initial independent estimates of style based on co-occurrence with seeds in a large corpus, and then refining those es-timates based on the relationship between styles. We compare various promising implementation options, including vector space, Bayesian, and graph-based repre-sentations, and conclude that a hybrid ap-proach is indeed warranted. 1