tailieunhanh - Báo cáo khoa học: "Unsupervised Event Coreference Resolution with Rich Linguistic Features"

This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects, these models consider a potentially infinite number of features and categorical outcomes. The evaluation performed for solving both within- and cross-document event coreference shows significant improvements of the models when compared against two baselines for this task. | Unsupervised Event Coreference Resolution with Rich Linguistic Features Cosmin Adrian Bejan Institute for Creative Technologies University of Southern California Marina del Rey CA 90292 USA Sanda Harabagiu Human Language Technology Institute University of Texas at Dallas Richardson TX 75083 USA Abstract This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects these models consider a potentially infinite number of features and categorical outcomes. The evaluation performed for solving both within- and cross-document event coreference shows significant improvements of the models when compared against two baselines for this task. 1 Introduction The event coreference task consists of finding clusters of event mentions that refer to the same event. Although it has not been extensively studied in comparison with the related problem of entity coreference resolution solving event coreference has already proved its usefulness in various applications such as topic detection and tracking Allan et al. 1998 information extraction Humphreys et al. 1997 question answering Narayanan and Harabagiu 2004 textual entailment Haghighi et al. 2005 and contradiction detection de Marneffe et al. 2008 . Previous approaches for solving event coreference relied on supervised learning methods that explore various linguistic properties in order to decide if a pair of event mentions is coreferential or not Humphreys et al. 1997 Bagga and Baldwin 1999 Ahn 2006 Chen and Ji 2009 . In spite of being successful for a particular labeled corpus these pairwise models are dependent on the domain or language that they are trained on. Moreover since event coreference resolution is a complex task that involves exploring a rich set of linguistic features annotating a large corpus with event coreference information for a new language or domain of interest requires