Coreference resolution with ILP-based weighted abduction

Naoya Inoue, Ekaterina Ovchinnikova, Kentaro Inui, Jerry Hobbs

Research output: Contribution to conferencePaperpeer-review

8 Citations (Scopus)


This paper presents the first systematic study of the coreference resolution problem in a general inference-based discourse processing framework. Employing the mode of inference called weighted abduction, we propose a novel solution to the overmerging problem inherent to inference-based frameworks. The overmerging problem consists in erroneously assuming distinct entities to be identical. In discourse processing, overmerging causes establishing wrong coreference links. In order to approach this problem, we extend Hobbs et al. (1993)'s weighted abduction by introducing weighted unification and show how to learn the unification weights by applying machine learning techniques. For making large-scale processing and parameter learning in an abductive logic framework feasible, we employ a new efficient implementation of weighted abduction based on Integer Linear Programming. We then propose several linguistically motivated features for blocking incorrect unifications and employ different large-scale world knowledge resources for establishing unification via inference. We provide a large-scale evaluation on the CoNLL-2011 shared task dataset, showing that all features and almost all knowledge components improve the performance of our system.

Original languageEnglish
Number of pages18
Publication statusPublished - 2012
Event24th International Conference on Computational Linguistics, COLING 2012 - Mumbai, India
Duration: 2012 Dec 82012 Dec 15


Conference24th International Conference on Computational Linguistics, COLING 2012


  • Coreference resolution
  • Integer linear programming
  • Weighted abduction


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