Paper: Text-level Discourse Dependency Parsing

ACL ID P14-1003
Title Text-level Discourse Dependency Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 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.