CODRA: A Novel Discriminative Framework for Rhetorical Analysis

• Published in: Computational linguistics - Association for Computational Linguistics 2015-09, Vol.41 (3), p.385-435
• Main Authors: Joty, Shafiq, Carenini, Giuseppe, Ng, Raymond T.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Construction; Discourse analysis; Parsers; Parsing algorithms; Representations; Rhetoric; State of the art; Texts; Trees
• ISSN: 0891-2017; 1530-9312
• DOI: 10.1162/COLI_a_00226

Clauses and sentences rarely stand on their own in an actual discourse; rather, the relationship between them carries important information that allows the discourse to express a meaning as a whole beyond the sum of its individual parts. Rhetorical analysis seeks to uncover this coherence structure. In this article, we present — a COmplete probabilistic Discriminative framework for performing Rhetorical Analysis in accordance with Rhetorical Structure Theory, which posits a tree representation of a discourse. comprises a discourse segmenter and a discourse parser. First, the discourse segmenter, which is based on a binary classifier, identifies the elementary discourse units in a given text. Then the discourse parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing. We present two approaches to combine these two stages of parsing effectively. By conducting a series of empirical evaluations over two different data sets, we demonstrate that significantly outperforms the state-of-the-art, often by a wide margin. We also show that a reranking of the k-best parse hypotheses generated by can potentially improve the accuracy even further.