Paper: Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification

ACL ID D09-1018
Title Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

This work investigates design choices in modeling a discourse scheme for im- proving opinion polarity classification. For this, two diverse global inference paradigms are used: a supervised collec- tive classification framework and an un- supervised optimization framework. Both approaches perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme. We also present quan- titative and qualitative analyses showing how the improvements are achieved.