Discovering Asymmetric Entailment Relations between Verbs Using Selectional Preferences

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 849–856, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Discovering asymmetric entailment relations between verbs using selectional preferences Fabio Massimo Zanzotto DISCo University of Milano-Bicocca Via Bicocca degli Arcimboldi 8, Milano, Italy zanzotto@disco.unimib.it Marco Pennacchiotti, Maria Teresa Pazienza ART Group - DISP University of Rome “Tor Vergata” Viale del Politecnico 1, Roma, Italy {pennacchiotti, pazienza}@info.uniroma2.it Abstract In this paper we investigate a novel method to detect asymmetric entailment relations between verbs. Our starting point is the idea that some point-wise verb selec- tional preferences carry relevant seman- tic information. Experiments using Word- Net as a gold standard show promising re- sults. Where applicable, our method, used in combination with other approaches, sig- nificantly increases the performance of en- tailment detection. A combined approach including our model improves the AROC of 5% absolute points with respect to stan- dard models. 1 Introduction Natural Language Processing applications often need to rely on large amount of lexical semantic knowledge to achieve good performances. Asym- metric verb relations are part of it. Consider for example the question “What college did Marcus Camby play for?”. A question answering (QA) system could find the answer in the snippet “Mar- cus Camby won for Massachusetts” as the ques- tion verb play is related to the verb win. The vice- versa is not true. If the question is “What college did Marcus Camby won for?”, the snippet “Mar- cus Camby played for Massachusetts” cannot be used. Winnig entails playing but not vice-versa, as the relation between win and play is asymmetric. Recently, many automatically built verb lexical- semantic resources have been proposed to sup- port lexical inferences, such as (Resnik and Diab, 2000; Lin and Pantel, 2001; Glickman and Dagan, 2003). All these resources focus on symmetric semantic relations, such as verb similarity. Yet, not enough attention has been paid so far to the study of asymmetric verb relations, that are often the only way to produce correct inferences, as the example above shows. In this paper we propose a novel approach to identify asymmetric relations between verbs. The main idea is that asymmetric entailment relations between verbs can be analysed in the context of class-level and word-level selectional preferences (Resnik, 1993). Selectional preferences indicate an entailment relation between a verb and its ar- guments. For example, the selectional preference {human} win may be read as a smooth constraint: if x is the subject of win then it is likely that x is a human, i.e. win(x) → human(x). It fol- lows that selectional preferences like {player} win may be read as suggesting the entailment relation win(x) → play(x). Selectional preferences have been often used to infer semantic relations among verbs and to build symmetric semantic resources as in (Resnik and Diab, 2000; Lin and Pantel, 2001; Glickman and Dagan, 2003). However, in those cases these are exploited in a different way. The assumption is that verbs are semantically related if …

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