CLAILOLOMay 5, 2019

A Typedriven Vector Semantics for Ellipsis with Anaphora using Lambek Calculus with Limited Contraction

arXiv:1905.01647v111 citations
Originality Incremental advance
AI Analysis

This addresses a specific limitation in computational linguistics for modeling ellipsis, but it is incremental as it builds on existing typelogical and distributional approaches.

The paper tackles the problem of modeling verb phrase ellipsis with anaphora in compositional distributional semantics by developing a vector semantics using Lambek calculus with limited contraction, resulting in a framework that allows word meanings to be copied as programs with non-linear access to embeddings.

We develop a vector space semantics for verb phrase ellipsis with anaphora using type-driven compositional distributional semantics based on the Lambek calculus with limited contraction (LCC) of Jäger (2006). Distributional semantics has a lot to say about the statistical collocation-based meanings of content words, but provides little guidance on how to treat function words. Formal semantics on the other hand, has powerful mechanisms for dealing with relative pronouns, coordinators, and the like. Type-driven compositional distributional semantics brings these two models together. We review previous compositional distributional models of relative pronouns, coordination and a restricted account of ellipsis in the DisCoCat framework of Coecke et al. (2010, 2013). We show how DisCoCat cannot deal with general forms of ellipsis, which rely on copying of information, and develop a novel way of connecting typelogical grammar to distributional semantics by assigning vector interpretable lambda terms to derivations of LCC in the style of Muskens & Sadrzadeh (2016). What follows is an account of (verb phrase) ellipsis in which word meanings can be copied: the meaning of a sentence is now a program with non-linear access to individual word embeddings. We present the theoretical setting, work out examples, and demonstrate our results on a toy distributional model motivated by data.

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