Disambiguation and filtering methods in using web knowledge for coreference resolution
|Disambiguation and filtering methods in using web knowledge for coreference resolution|
|Author(s)||Uryupina O., Poesio M., Giuliano C., Tymoshenko K.|
|Published in||Proceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24|
|Keyword(s)||Unknown (Extra: Aliasing, Coreference resolution, CR system, Evaluation experiments, Filtering method, Knowledge basis, Percentage points, Performance level, Semantic compatibility, Semantic information, System's performance, Web resources, Wikipedia, Artificial intelligence, Semantic Web, Semantics, User interfaces, Websites, Knowledge management)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Disambiguation and filtering methods in using web knowledge for coreference resolution is a 2011 conference paper written in English by Uryupina O., Poesio M., Giuliano C., Tymoshenko K. and published in Proceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24.
We 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 (CR) engine. We extract semantic compatibility and aliasing information from Wikipedia and Yago, and incorporate it into a CR system. We show that using such knowledge with no disambiguation and filtering does not bring any improvement over the baseline, mirroring the previous findings (Ponzetto and Poesio 2009). We 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. Our evaluation experiments on the ACE-02 corpus show that the knowledge, extracted from Wikipedia and Yago, improves our system's performance by 2-3 percentage points. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 3 time(s)