We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8% over a state-ofthe-art decision-based discourse parser. A set of empirical evaluations shows that our discourse parsing model is sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance.
REF developed a sentence-level discourse parser using syntactic and lexical features and showed a strong correlation between syntactic and discourse information.
Sentence Level Discourse Parsing Using Syntactic And Lexical Information
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science