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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.
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