A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution
Tipo de documentoArtículo
Fecha de publicación2013-12
Condiciones de accesoAcceso abierto
Proyecto de la Comisión EuropeaFAUST - Feedback Analysis for User adaptive Statistical Translation (EC-FP7-247762)
This work is focused on research in machine learning for coreference resolution. Coreference resolution is a natural language processing task that consists of determining the expressions in a discourse that refer to the same entity. The main contributions of this article are (i) a new approach to coreference resolution based on constraint satisfaction, using a hypergraph to represent the problem and solving it by relaxation labeling; and (ii) research towards improving coreference resolution performance using world knowledge extracted from Wikipedia. The developed approach is able to use an entity-mention classification model with more expressiveness than the pair-based ones, and overcome the weaknesses of previous approaches in the state of the art such as linking contradictions, classifications without context, and lack of information evaluating pairs. Furthermore, the approach allows the incorporation of new information by adding constraints, and research has been done in order to use world knowledge to improve performances. RelaxCor, the implementation of the approach, achieved results at the state-of-the-art level, and participated in international competitions: SemEval-2010 and CoNLL-2011. RelaxCor achieved second place in CoNLL-2011.
CitaciónSapena, E.; Padro, L.; Turmo, J. A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution. "Computational linguistics", Desembre 2013, vol. 39, núm. 4, p. 847-884.
Versión del editorhttp://www.mitpressjournals.org/toc/coli/39/4