Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15239
Title: A Twin-Candidate Model for Learning Based Coreference Resolution
Authors: YANG XIAOFENG
Keywords: coreference resolution, anaphora resolution, reference, twin-candidate model, machine learning, natural language processing
Issue Date: 23-Mar-2006
Source: YANG XIAOFENG (2006-03-23). A Twin-Candidate Model for Learning Based Coreference Resolution. ScholarBank@NUS Repository.
Abstract: The purpose of the thesis is to find an effective learning model for coreference resolution. The traditional single-candidate model only considers one individual antecedent candidate at a time for its learning, and thus cannot capture the preference relationship between competing candidates. To overcome this limitation, the thesis proposes a twin-candidate learning model which recasts antecedent selection as a preference classification problem. The model learns a classifier that can determine the preference between two competing antecedent candidates of an anaphor, and then choose the antecedent based on the ranking of the candidates. The thesis explores how to use the twin-candidate model to identify the antecedent from the candidates of an anaphor. Further it investigates how to deploy this model to coreference resolution. Also it discusses how to represent the preference knowledge in the model. The experimental results indicate that the model is effective for both anaphora resolution and coreference resolution.
URI: http://scholarbank.nus.edu.sg/handle/10635/15239
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
YangXF.pdf530.62 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

206
checked on Dec 11, 2017

Download(s)

197
checked on Dec 11, 2017

Google ScholarTM

Check


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