Polarity classification of opinionated sentences with both positive and negative sentiments 1 is a key challenge in sentiment analysis. This paper presents a novel unsupervised method for discovering intra-sentence level discourse relations for eliminating polarity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was defined empirically based on Rhetorical Structure Theory (RST). Then, a small set of cuephrase-based patterns were utilized to collect a large number of discourse instances which were later converted to semantic sequential representations (SSRs). Finally, an unsupervised method was adopted to generate, weigh and filter new SSRs without cue phrases for recognizing discourse relations. Experimental results showed that the proposed methods not only effectively recognized the defined discourse relations but also achieved significant improvement by integrating discourse information in sentence-level polarity classification.
REF used the semantic sequential representations to recognize RST-based discourse relations for eliminating intra-sentence polarity ambiguities.
Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities
EMNLP
Computer Science