Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41240
Title: Kernel based discourse relation recognition with temporal ordering information
Authors: Wang, W.T.
Su, J.
Tan, C.L. 
Issue Date: 2010
Source: Wang, W.T.,Su, J.,Tan, C.L. (2010). Kernel based discourse relation recognition with temporal ordering information. ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference : 710-719. ScholarBank@NUS Repository.
Abstract: Syntactic knowledge is important for discourse relation recognition. Yet only heuristically selected flat paths and 2-level production rules have been used to incorporate such information so far. In this paper we propose using tree kernel based approach to automatically mine the syntactic information from the parse trees for discourse analysis, applying kernel function to the tree structures directly. These structural syntactic features, together with other normal flat features are incorporated into our composite kernel to capture diverse knowledge for simultaneous discourse identification and classification for both explicit and implicit relations. The experiment shows tree kernel approach is able to give statistical significant improvements over flat syntactic path feature. We also illustrate that tree kernel approach covers more structure information than the production rules, which allows tree kernel to further incorporate information from a higher dimension space for possible better discrimination. Besides, we further propose to leverage on temporal ordering information to constrain the interpretation of discourse relation, which also demonstrate statistical significant improvements for discourse relation recognition on PDTB 2.0 for both explicit and implicit as well. © 2010 Association for Computational Linguistics.
Source Title: ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/41240
ISBN: 9781617388088
Appears in Collections:Staff Publications

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