Unsupervised Event Coreference Resolution with Rich Linguistic Features

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1412–1422, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics 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 ef- fectively applied to an open-domain event coreference task. Designed with the pur- pose of clustering complex linguistic ob- jects, 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 im- provements 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 stud- ied in comparison with the related problem of en- tity coreference resolution, solving event coref- erence has already proved its usefulness in vari- ous applications such as topic detection and track- ing (Allan et al., 1998), information extraction (Humphreys et al., 1997), question answering (Narayanan and Harabagiu, 2004), textual entail- ment (Haghighi et al., 2005), and contradiction de- tection (de Marneffe et al., 2008). Previous approaches for solving event corefer- ence relied on supervised learning methods that explore various linguistic properties in order to de- cide if a pair of event mentions is coreferential or not (Humphreys et al., 1997; Bagga and Bald- win, 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 a substantial amount of manual effort. Also, since these models are de- pendent on local pairwise decisions, they are un- able to capture a global event distribution at topic or document collection level. To address these limitations and to provide a more flexible representation for modeling observ- able data with rich properties, we present two novel, fully generative, nonparametric Bayesian models for unsupervised within- and cross- document event coreference resolution. The first model extends the hierarchical Dirichlet process (Teh et al., 2006) to take into account additional properties associated with observable objects (i.e., event mentions). The second model overcomes some of the limitations of the first model. uses the infinite factorial hidden Markov modelIt (Van Gael et al., 2008b) coupled to the infinite hidden Markov model (Beal et al., 2002) in or- der to (1) consider a potentially infinite number of features associated with observable objects, (2) perform an automatic selection of the most salient features, and (3) capture the structural dependen- cies of observable objects at …

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