Graph-Based Bootstrapping for Coreference Resolution

• Main Authors: Balaji J., Geetha T.V., Ranjani P.
• Format: Article
• Language: English
• Published: De Gruyter 2014-09-01
• Series: Journal of Intelligent Systems
• Subjects: coreference resolution; bootstrapping; semantic graphs; universal networking language (unl); 2010 mathematics subject classification: 68 computer science, 68t50 natural language processing
• Online Access: https://doi.org/10.1515/jisys-2013-0056

Coreference resolution is a challenging natural language processing task, and it is difficult to identify the correct mentions of an entity that can be any noun or noun phrase. In this article, a semisupervised, two-stage pattern-based bootstrapping approach is proposed for the coreference resolution task. During Stage 1, the possible mentions are identified using word-based features, and during Stage 2, the correct mentions are identified by filtering the non-coreferents of an entity using statistical measures and graph-based features. Whereas the existing approaches use morphosyntactic and number/gender agreement features, the proposed approach uses semantic graph-based context-level semantics and nested noun phrases in the correct mentions identification. Moreover, mentions without the number/gender information are identified, using the context-based features of the semantic graph. The evaluation performed for the coreference resolution shows significant improvements, when compared with the word association-based bootstrapping systems.