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, RIP-PER 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.
REF proposed a first-order logic learning approach to relation classification using lexical and linguistic information and compositional semantics.
An effective Discourse Parser that uses Rich Linguistic Information
HLT-NAACL
Computer Science