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A graph partitioning approach to entity disambiguation using uncertain information

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Sapena Masip, Emilio
Padró, LluísMés informacióMés informacióMés informació
Turmo Borras, JorgeMés informacióMés informacióMés informació
Document typeConference report
Defense date2008-08-31
PublisherSpringer
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
This paper presents a method for Entity Disambiguation in Information Extraction from different sources in the web. Once entities and relations between them are extracted, it is needed to determine which ones are referring to the same real-world entity. We model the problem as a graph partitioning problem in order to combine the available information more accurately than a pairwise classifier. Moreover, our method handle uncertain information which turns out to be quite helpful. Two algorithms are trained and compared, one probabilistic and the other deterministic. Both are tuned using genetic algorithms to find the best weights for the set of constraints. Experiments show that graph-based modeling yields better results using uncertain information.
CitationSapena, E.; Padró, L.; Turmo, J. A graph partitioning approach to entity disambiguation using uncertain information. A: 6th International Conference Advances in Natural Language Processing. "6th International Conference Advances in Natural Language Processing". Springer, 2008, p. 428-439. 
URIhttp://hdl.handle.net/2117/7552
ISBN978-3-540-85286-5
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  • GPLN - Grup de Processament del Llenguatge Natural - Ponències/Comunicacions de congressos [192]
  • Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.191]
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