Lexical Inference over Multi-Word Predicates: A Distributional Approach

Abstract

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.

Learning Typed Entailment Graphs with Global Soft Constraints

“…Our work injects two other types of prior knowledge about the structure of the graph that are less expensive to incorporate and yield better results on entailment rule data sets. Abend et al (2014) learn entailment relations over multi-word predicates with different levels of compositionality. Pavlick et al (2015) add variety of relations, including entailment, to phrase pairs in PPDB.…”