Entailment above the word level in distributional semantics

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 23–32, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Entailment above the word level in distributional semantics Marco Baroni Raffaella Bernardi University of Trento name.surname@unitn.it Ngoc-Quynh Do Free University of Bozen-Bolzano quynhdtn.hut@gmail.com Chung-chieh Shan Cornell University University of Tsukuba ccshan@post.harvard.edu Abstract We introduce two ways to detect entail- ment using distributional semantic repre- sentations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our second experiment shows that a classi- fier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|=some dogs) to entailment involving unseen quantifiers (all cats|=several cats). Moreover, nominal and quantifier phrase entailment appears to be cued by different distributional corre- lates, as predicted by the type-based view of entailment in formal semantics. 1 Introduction Distributional semantics (DS) approximates lin- guistic meaning with vectors summarizing the contexts where expressions occur. The success of DS in lexical semantics has validated the hy- pothesis that semantically similar expressions oc- cur in similar contexts (Landauer and Dumais, 1997; Lund and Burgess, 1996; Sahlgren, 2006; Sch¨utze, 1997; Turney and Pantel, 2010). For- mal semantics (FS) represents linguistic mean- ings as symbolic formulas and assemble them via composition rules. FS has successfully modeled quantification and captured inferential relations between phrases and between sentences (Mon- tague, 1970; Thomason, 1974; Heim and Kratzer, 1998). The strengths of DS and FS have been complementary to date: On one hand, DS has in- duced large-scale semantic representations from corpora, but it has been largely limited to the lexical domain. On the other hand, FS has pro- vided sophisticated models of sentence meaning, but it has been largely limited to hand-coded mod- els that do not scale up to real-life challenges by learning from data. Given these complementary strengths, we nat- urally ask if DS and FS can address each other’s limitations. Two recent strands of research are bringing DS closer to meeting core FS chal- lenges. One strand attempts to model compo- sitionality with DS methods, representing both primitive and composed linguistic expressions as distributional vectors (Baroni and Zamparelli, 2010; Grefenstette and Sadrzadeh, 2011; Gue- vara, 2010; Mitchell and Lapata, 2010). The other strand attempts to reformulate FS’s notion of logical inference in terms that DS can cap- ture (Erk, 2009; Geffet and Dagan, 2005; Kotler- man et al., 2010; Zhitomirsky-Geffet and Dagan, 2010). In keeping with the lexical emphasis of DS, this strand has focused on inference at the word level, or lexical entailment, that is, discover- ing from distributional vectors of hyponyms (dog) that they entail their hypernyms (animal). This paper brings these two strands of research together by demonstrating two ways in which the distributional vectors of composite expressions bear on inference. Here we focus on phrasal vec- tors harvested directly …

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