Text-level Discourse Dependency Parsing
Sujian Li, Liang Wang, Ziqiang Cao, Wenjie Li · 2014
Previous researches on Text-level discourse parsing mainly made use of constituency structure to parse the whole document into one discourse tree. In this paper, we present the limitations of constituency based dis-course parsing and first propose to use de-pendency structure to directly represent the relations between elementary discourse units (EDUs). The state-of-the-art depend-ency parsing techniques, the Eisner algo-rithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arc-factored model and the large-margin learn-ing techniques. Experiments show that our discourse dependency parsers achieve a competitive performance on text-level dis-course parsing. 1