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An effective Discourse Parser that uses Rich Linguistic Information

31 May 2009Proceedings 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. 2009 5archive 2025-07-28

Rajen Subba, Barbara Di Eugenio

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.

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