Distributional semantics provides multidimensional, graded, empirically induced word
representations that successfully capture many aspects of meaning in natural languages, as …

Deep learning has recently come to dominate computational linguistics, leading to claims of
human-level performance in a range of language processing tasks. Like much previous …

The lexicon of any natural language encodes a huge number of distinct word meanings. Just
to understand this article, you will need to know what thousands of words mean. The space …

In this paper we address the problem of modeling compositional meaning for phrases and
sentences using distributional methods. We experiment with several possible combinations …

Given appropriate representations of the semantic relations between carpenter and wood
and between mason and stone (for example, vectors in a vector space model), a suitable …

This article develops a compositional vector-based semantics of subject and object relative
pronouns within a categorical framework. Frobenius algebras are used to formalize the …

We provide a comparative study between neural word representations and traditional vector
spaces based on co-occurrence counts, in a number of compositional tasks. We use three …

We present a model for compositional distributional semantics related to the framework of
Coecke et al.(2010), and emulating formal semantics by representing functions as tensors …

Abstract The Distributional Compositional Categorical (DisCoCat) model is a mathematical
framework that provides compositional semantics for meanings of natural language …

Recent work has shown that compositionaldistributional models using element-wise
operations on contextual word vectors benefit from the introduction of a prior disambiguation …