An effective Discourse Parser that uses Rich Linguistic Information
2009-05-31Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 566–574, Boulder, Colorado. Association for Computational Linguistics. 2009Unverified0· sign in to hype
Rajen Subba, Barbara Di Eugenio
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This paper presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in classifying relations over attribute-value learning paradigms such as Decision Trees, RIPPER and Naive Bayes. For discourse parsing, our modified shift-reduce parsing model that uses our relation classifier significantly outperforms a right-branching majority-class baseline.