Paper: An effective Discourse Parser that uses Rich Linguistic Information

ACL ID N09-1064
Title An effective Discourse Parser that uses Rich Linguistic Information
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

This paper presents a first-order logic learn- ing approach to determine rhetorical relations between discourse segments. Beyond lin- guistic cues and lexical information, our ap- proach exploits compositional semantics and segment discourse structure data. We report a statistically significant improvement in clas- sifying relations over attribute-value learn- ing paradigms such as Decision Trees, RIP- PER and Naive Bayes. For discourse pars- ing, our modified shift-reduce parsing model that uses our relation classifier significantly outperforms a right-branching majority-class baseline.