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.
Titolo: | Disambiguation and filtering methods in using web knowledge for coreference resolution |
Autori: | Uryupina, Olga; Poesio, Massimo; C., Giuliano; Tymoshenko, Kateryna |
Autori Unitn: | |
Titolo del volume contenente il saggio: | Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence |
Luogo di edizione: | Palo Alto, CA, USA |
Casa editrice: | American Association for Artificial Intelligence (AAAI) |
Anno di pubblicazione: | 2011 |
Codice identificativo Scopus: | 2-s2.0-80052403950 |
ISBN: | 9781577355014 |
Handle: | http://hdl.handle.net/11572/89837 |
Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |