Abstract
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 discourse parsing and first propose to use dependency structure to directly represent the relations between elementary discourse units (EDUs). The state-of-the-art dependency parsing techniques, the Eisner algorithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arcfactored model and the large-margin learning techniques. Experiments show that our discourse dependency parsers achieve a competitive performance on text-level discourse parsing.
Original language | English |
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Title of host publication | Long Papers |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 25-35 |
Number of pages | 11 |
Volume | 1 |
ISBN (Print) | 9781937284725 |
Publication status | Published - 1 Jan 2014 |
Event | 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States Duration: 22 Jun 2014 → 27 Jun 2014 |
Conference
Conference | 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 |
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Country/Territory | United States |
City | Baltimore, MD |
Period | 22/06/14 → 27/06/14 |
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language