An effective discourse parser that uses rich linguistic information

Authors:
Rajen Subba;Barbara Di Eugenio
Affiliations:
Yahoo! Labs, Sunnyvale, CA;University of Illinois, Chicago, IL
Venue:
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2009

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Abstract

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.