A Novel Discriminative Framework for Sentence-Level Discourse Analysis.

We propose a complete probabilistic discriminative framework for performing sentence-level discourse analysis. Our framework comprises a discourse segmenter, based on a binary classifier, and a discourse parser, which applies an optimal CKY-like parsing algorithm to probabilities inferred from a Dynamic Conditional Random Field. We show on two corpora that our approach outperforms the state-of-the-art, often by a wide margin.