Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

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

Why our use of language is highly creative yet also constrained We use words and phrases
creatively to express ourselves in ever-changing contexts, readily extending language …

Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …

This paper reviews the state-of-the-art of one emergent field in computational linguistics—
semantic change computation. It summarizes the literature by proposing a framework that …

Most words in natural languages are polysemous; that is, they have related but different
meanings in different contexts. This one‐to‐many mapping of form to meaning presents a …

We have evolved to become who we are, at least in part, due to our general drive to create
new things and ideas. When seeking to improve our creations, ideas, or situations, we …

Distributional semantics embodies the idea that the context in which a word occurs reveals
the meaning of that word. In contemporary corpus linguistics, that idea takes shape in …

Diachronic study of the evolution of languages is of importance in natural language
processing (NLP). Recent years have witnessed a surge of computational approaches for …

This paper presents token-based semantic vector spaces as a tool that can be applied in
corpus-linguistic analyses such as word sense comparisons, comparisons of synonymous …