Potential and limits of distributional approaches for semantic relatedness

Distributional models assume that the contexts of a linguistic unit (such as a word, a multi-word expression, a phrase, a sentence, etc.) provide information about the meaning of the linguistic unit (Firth, 1957; Harris, 1968). They have been widely applied in data-intensive lexical semantics (among other areas), and proven successful in diverse research issues, such as the representation and disambiguation of word senses (Schütze, 1998; McCarthy et al., 2004; Springorum et al., 2013), selectional preference modelling (Herdagdelen and Baroni, 2009; Erk et al., 2010; Schulte im Walde, 2010), the compositionality of compounds and phrases (McCarthy et al., 2003; Reddy et al., 2011; Boleda et al., 2013), or as a general framework across semantic tasks (’distributional memory’, cf. Baroni and Lenci, 2010; Pado and Utt, 2012), to name just a few examples. While it is clear that distributional knowledge does not cover all the cognitive knowledge humans possess with respect to word meaning (Marconi, 1997; Lenci, 2008), distributional models are very attractive, as the underlying parameters are accessible from even low-level annotated corpus data. We are thus interested in maximising the benefit of distributional information for lexical semantics, by exploring the meaning and the potential of comparatively simple distributional models. In this respect, this talk will present four case studies on semantic relatedness tasks that demonstrate the potential and the limits of distributional models.