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 …

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence,
it is important to understand the biases present in their outputs in order to avoid perpetuating …

Word embeddings are real-valued word representations able to capture lexical semantics
and trained on natural language corpora. Models proposing these representations have …

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 …

Cognitive Neuroscience of Language provides an up-to-date, wide-ranging, and
pedagogically practical survey of the most important developments in this exciting field. It …

Recent advances have made it possible to precisely measure the extent to which any two
words are used in similar contexts. In turn, this measure of similarity in linguistic context also …

We combined established psychological measures with techniques in machine learning to
measure changes in gender stereotypes over the course of the 20th century as expressed in …

A lively research field has recently emerged that uses experimental methods to probe the
linguistic behaviour of modern deep networks. While work in this tradition often reports …

Many complex problems are naturally understood in terms of symbolic concepts. For
example, our concept of “cat” is related to our concepts of “ears” and “whiskers” in a non …