Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2011

Abstract

Polarity classification of opinionated sentences with both positive and negative sentiments1 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.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP 2011)

First Page

162

Last Page

171

Publisher

Association for Computational Linguistics

City or Country

Edinburgh, Scotland, UK

Additional URL

https://aclweb.org/anthology/D11-1015

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