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Disambiguation and Filtering Methods in Using Web Knowledge for Coreference Resolution
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Author(s): Olga Uryupina (University of Trento, Italy), Massimo Poesio (University of Trento, Italy & University of Essex, UK), Claudio Giuliano (University of Trento, Italy & Fondazione Bruno Kessler, Italy)and Kateryna Tymoshenko (University of Trento, Italy & Fondazione Bruno Kessler, Italy)
Copyright: 2012
Pages: 17
Source title:
Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches
Source Author(s)/Editor(s): Chutima Boonthum-Denecke (Hampton University, USA), Philip M. McCarthy (The University of Memphis, USA)and Travis Lamkin (University of Memphis, USA)
DOI: 10.4018/978-1-61350-447-5.ch013
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Abstract
The authors investigate two publicly available Web knowledge bases, Wikipedia and Yago, in an attempt to leverage semantic information and increase the performance level of a state-of-the-art coreference resolution engine. They extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a coreference resolution system. The authors show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings (Ponzetto & Poesio, 2009). They propose, therefore, a number of solutions to reduce the amount of noise coming from Web resources: using disambiguation tools for Wikipedia, pruning Yago to eliminate the most generic categories and imposing additional constraints on affected mentions. The evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves the system’s performance by 2-3 percentage points.
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