A Latent Dirichlet Allocation Method for Selectional Preferences

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 424–434, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics A Latent Dirichlet Allocation method for Selectional Preferences Alan Ritter, Mausam and Oren Etzioni Department of Computer Science and Engineering Box 352350, University of Washington, Seattle, WA 98195, USA {aritter,mausam,etzioni}@cs.washington.edu Abstract The computation of selectional prefer- ences, the admissible argument values for a relation, is a well-known NLP task with broad applicability. We present LDA-SP, which utilizes LinkLDA (Erosheva et al., 2004) to model selectional preferences. By simultaneously inferring latent top- ics and topic distributions over relations, LDA-SP combines the benefits of pre- vious approaches: like traditional class- based approaches, it produces human- interpretable classes describing each re- lation’s preferences, but it is competitive with non-class-based methods in predic- tive power. We compare LDA-SP to several state-of- the-art methods achieving an 85% increase in recall at 0.9 precision over mutual in- formation (Erk, 2007). We also eval- uate LDA-SP’s effectiveness at filtering improper applications of inference rules, where we show substantial improvement over Pantel et al.’s system (Pantel et al., 2007). 1 Introduction Selectional Preferences encode the set of admissi- ble argument values for a relation. For example, locations are likely to appear in the second argu- ment of the relation X is headquartered in Y and companies or organizations in the first. A large, high-quality database of preferences has the po- tential to improve the performance of a wide range of NLP tasks including semantic role labeling (Gildea and Jurafsky, 2002), pronoun resolution (Bergsma et al., 2008), textual inference (Pantel et al., 2007), word-sense disambiguation (Resnik, 1997), and many more. Therefore, much atten- tion has been focused on automatically computing them based on a corpus of relation instances. Resnik (1996) presented the earliest work in this area, describing an information-theoretic ap- proach that inferred selectional preferences based on the WordNet hypernym hierarchy. Recent work (Erk, 2007; Bergsma et al., 2008) has moved away from generalization to known classes, instead utilizing distributional similarity between nouns to generalize beyond observed relation-argument pairs. This avoids problems like WordNet’s poor coverage of proper nouns and is shown to improve performance. These methods, however, no longer produce the generalized class for an argument. In this paper we describe a novel approach to computing selectional preferences by making use of unsupervised topic models. Our approach is able to combine benefits of both kinds of meth- ods: it retains the generalization and human- interpretability of class-based approaches and is also competitive with the direct methods on pre- dictive tasks. Unsupervised topic models, such as latent Dirichlet allocation (LDA) (Blei et al., 2003) and its variants are characterized by a set of hidden topics, which represent the underlying semantic structure of a document collection. For our prob- lem these topics offer an intuitive interpretation – they represent the (latent) set of classes that store the preferences for the different relations. Thus, topic models are a natural fit for modeling our re- lation data. …

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