A Bayesian Approach to Unsupervised Semantic Role Induction

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 12–22, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics A Bayesian Approach to Unsupervised Semantic Role Induction Ivan Titov Alexandre Klementiev Saarland University Saarbr¨ucken, Germany {titov|aklement}@mmci.uni-saarland.de Abstract We introduce two Bayesian models for un- supervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. The first model induces these clusterings independently for each predicate, exploit- ing the Chinese Restaurant Process (CRP) as a prior. In a more refined hierarchical model, we inject the intuition that the clus- terings are similar across different predi- cates, even though they are not necessar- ily identical. This intuition is encoded as a distance-dependent CRP with a distance between two syntactic signatures indicating how likely they are to correspond to a single semantic role. These distances are automat- ically induced within the model and shared across predicates. Both models achieve state-of-the-art results when evaluated on PropBank, with the coupled model consis- tently outperforming the factored counter- part in all experimental set-ups. 1 Introduction Semantic role labeling (SRL) (Gildea and Juraf- sky, 2002), a shallow semantic parsing task, has recently attracted a lot of attention in the com- putational linguistic community (Carreras and M`arquez, 2005; Surdeanu et al., 2008; Hajiˇc et al., 2009). The task involves prediction of predi- cate argument structure, i.e. both identification of arguments as well as assignment of labels accord- ing to their underlying semantic role. For exam- ple, in the following sentences: (a) [A0 Mary] opened [A1 the door]. (b) [A0 Mary] is expected to open [A1 the door]. (c) [A1 The door] opened. (d) [A1 The door] was opened [A0 by Mary]. Mary always takes an agent role (A0) for the pred- icate open, and door is always a patient (A1). SRL representations have many potential appli- cations in natural language processing and have recently been shown to be beneficial in question answering (Shen and Lapata, 2007; Kaisser and Webber, 2007), textual entailment (Sammons et al., 2009), machine translation (Wu and Fung, 2009; Liu and Gildea, 2010; Wu et al., 2011; Gao and Vogel, 2011), and dialogue systems (Basili et al., 2009; van der Plas et al., 2011), among others. Though syntactic representations are often predic- tive of semantic roles (Levin, 1993), the interface between syntactic and semantic representations is far from trivial. The lack of simple determinis- tic rules for mapping syntax to shallow semantics motivates the use of statistical methods. Although current statistical approaches have been successful in predicting shallow seman- tic representations, they typically require large amounts of annotated data to estimate model pa- rameters. These resources are scarce and ex- pensive to create, and even the largest of them have low coverage (Palmer and Sporleder, 2010). Moreover, these models are domain-specific, and their performance drops substantially when they are used in a new domain (Pradhan et al., 2008). Such domain specificity is arguably …

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