Predicting Strong Associations on the Basis of Corpus Data

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 648–656, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Predicting Strong Associations on the Basis of Corpus Data Yves Peirsman Research Foundation – Flanders & QLVL, University of Leuven Leuven, Belgium yves.peirsman@arts.kuleuven.be Dirk Geeraerts QLVL, University of Leuven Leuven, Belgium dirk.geeraerts@arts.kuleuven.be Abstract Current approaches to the prediction of associations rely on just one type of in- formation, generally taking the form of either word space models or collocation measures. At the moment, it is an open question how these approaches compare to one another. In this paper, we will investigate the performance of these two types of models and that of a new ap- proach based on compounding. The best single predictor is the log-likelihood ratio, followed closely by the document-based word space model. We will show, how- ever, that an ensemble method that com- bines these two best approaches with the compounding algorithm achieves an in- crease in performance of almost 30% over the current state of the art. 1 Introduction Associations are words that immediately come to mind when people hear or read a given cue word. For instance, a word like pepper calls up salt, and wave calls up sea. Aitchinson (2003) and Schulte im Walde and Melinger (2005) show that such associations can be motivated by a number of factors, from semantic similarity to colloca- tion. Current computational models of associa- tion, however, tend to focus on one of these, by us- ing either collocation measures (Michelbacher et al., 2007) or word space models (Sahlgren, 2006; Peirsman et al., 2008). To this day, two gen- eral problems remain. First, the literature lacks a comprehensive comparison between these gen- eral types of models. Second, we are still looking for an approach that combines several sources of information, so as to correctly predict a larger va- riety of associations. Most computational models of semantic rela- tions aim to model semantic similarity in particu- lar (Landauer and Dumais, 1997; Lin, 1998; Pad´o and Lapata, 2007). In Natural Language Process- ing, these models have applications in fields like query expansion, thesaurus extraction, informa- tion retrieval, etc. Similarly, in Cognitive Science, such models have helped explain neural activa- tion (Mitchell et al., 2008), sentence and discourse comprehension (Burgess et al., 1998; Foltz, 1996; Landauer and Dumais, 1997) and priming patterns (Lowe and McDonald, 2000), to name just a few examples. However, there are a number of appli- cations and research fields that will surely bene- fit from models that target the more general phe- nomenon of association. For instance, automat- ically predicted associations may prove useful in models of information scent, which seek to ex- plain the paths that users follow in their search for relevant information on the web (Chi et al., 2001). After all, if the visitor of a web shop clicks on music to find the prices of iPods, this behaviour is motivated by an associative relation different from similarity. …

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