Lexical Inference over Multi-Word Predicates: A Distributional Approach
Omri Abend, Shay B. Cohen, Mark J. Steedman · 2014
Representing predicates in terms of their argument distribution is common practice in NLP.Multi-word predicates (MWPs) in this context are often either disregarded or considered as fixed expressions.The latter treatment is unsatisfactory in two ways: (1) identifying MWPs is notoriously difficult, (2) MWPs show varying degrees of compositionality and could benefit from taking into account the identity of their component parts.We propose a novel approach that integrates the distributional representation of multiple sub-sets of the MWP's words.We assume a latent distribution over sub-sets of the MWP, and estimate it relative to a downstream prediction task.Focusing on the supervised identification of lexical inference relations, we compare against state-of-the-art baselines that consider a single sub-set of an MWP, obtaining substantial improvements.To our knowledge, this is the first work to address lexical relations between MWPs of varying degrees of compositionality within distributional semantics.