Sequence Models and Ranking Methods for Discourse Parsing

Sequence Models and Ranking Methods for Discourse Parsing A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Ben Wellner Many important aspects of natural language reside beyond the level of a single sentence or clause, at the level of the discourse, including: reference relations such anaphora, notions of topic/focus and foreground/background information as well as rhetorical relations such as Causation or Motivation. This dissertation is concerned with data-driven, machine learning-based methods for the latter – the identification of rhetorical discourse relations between abstract objects, including events, states and propositions. Our focus is specifically on those relations based on the semantic content of their arguments as opposed to the intent of the writer. We formulate a dependency view of discourse in which the arguments of a rhetorical relation are lexical heads, rather than arbitrary segments of text. This avoids the difficult problem of identifying the most elementary segments of the discourse. The resulting discourse parsing problem involves the following steps: 1) identification of discourse cue phrases that signal a rhetorical relation 2) identification of the two arguments of a rhetorical relation signaled by a discourse cue phrase and 3) determination of the type of the rhetorical relation.