RST-style Discourse Parsing and Its Applications in Discourse Analysis
Vanessa Wei Feng · TSpace (University of Toronto) · 2015
Discourse parsing is the task of identifying the relatedness and the particular discourse relations among various discourse units in a text. In particular, among various theoretical frameworks of discourse parsing, I am interested in Rhetorical Structure Theory (RST). I hypothesize that, given its ultimate success, discourse parsing can provide a general solution for use in many downstream applications.This thesis is composed of two major parts. First, I overview my work on discourse segmentation and discourse tree-building, which are the two primary components of RST-style discourse parsing. Evaluated on the RST Discourse Treebank (RST-DT), both of my discourse segmenter and tree-builder achieve the state-of-the-art performance.Later, I discuss the application of discourse relations to some specific tasks in the analysis of discourse, including the evaluation of coherence, the identification of authorship, and the detection of deception. In particular, I propose to use a set of application-neutral features, which are derived from the discourse relations extracted by my discourse parser, and compare the performance of these application-neutral features against the classic application-specific approaches to each of these tasks. On the first two tasks, experimental results show that discourse relation features by themselves often perform as well as those classic application-specific features, and the combination of these two kinds of features usually yields further improvement. These results provide strong evidence for my hypothesis that discourse parsing is able to provide a general solution for the analysis of discourse. However, we failed to observe a similar effectiveness of discourse parsing on the third task, the detection of deception. I postulate that this might be due to several confounding factors of the task itself.