Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41578
Title: A twin-candidate model of coreference resolution with non-anaphor identification capability
Authors: Yang, X. 
Su, J.
Tan, C.L. 
Issue Date: 2005
Source: Yang, X.,Su, J.,Tan, C.L. (2005). A twin-candidate model of coreference resolution with non-anaphor identification capability. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3651 LNAI : 719-730. ScholarBank@NUS Repository.
Abstract: Although effective for antecedent determination, the traditional twin-candidate model can not prevent the invalid resolution of non-anaphors without additional measures. In this paper we propose a modified learning framework for the twin-candidate model. In the new framework, we make use of non-anaphors to create a special class of training instances, which leads to a classifier capable of identifying the cases of non-anaphors during resolution. In this way, the twin-candidate model itself could avoid the resolution of non-anaphors, and thus could be directly deployed to coreference resolution. The evaluation done on newswire domain shows that the twin-candidate based system with our modified framework achieves better and more reliable performance than those with other solutions. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41578
ISBN: 3540291725
ISSN: 03029743
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

37
checked on Dec 9, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.