Conference Paper

Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution.

Conference: Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada.
Source: DBLP

ABSTRACT We present a sequence of unsupervised, nonparametric Bayesian models for clus- tering complex linguistic objects. In this approach, we consider a potentially infi- nite number of features and categorical outcomes. We evaluated these models for the task of within- and cross-document event coreference on two corpora. All the models we investigated show significant improvements when c ompared against an existing baseline for this task.

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    ABSTRACT: Coreference resolution tries to identify all expressions (called mentions) in observed text that refer to the same entity. Beside entity extraction and relation extraction, it represents one of the three complementary tasks in Information Extraction. In this paper we describe a novel coreference resolution system SkipCor that reformulates the problem as a sequence labeling task. None of the existing supervised, unsupervised, pairwise or sequence-based models are similar to our approach, which only uses linear-chain conditional random fields and supports high scalability with fast model training and inference, and a straightforward parallelization. We evaluate the proposed system against the ACE 2004, CoNLL 2012 and SemEval 2010 benchmark datasets. SkipCor clearly outperforms two baseline systems that detect coreferentiality using the same features as SkipCor. The obtained results are at least comparable to the current state-of-the-art in coreference resolution.
    PLoS ONE 06/2014; 9(6):e100101. · 3.53 Impact Factor

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