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 …

The success of the large neural language models on many NLP tasks is exciting. However,
we find that these successes sometimes lead to hype in which these models are being …

We consider the problem of learning general-purpose, paraphrastic sentence embeddings
based on supervision from the Paraphrase Database (Ganitkevitch et al., 2013). We …

Unsupervised methods for learning distributed representations of words are ubiquitous in
today's NLP research, but far less is known about the best ways to learn distributed phrase …

Abstract Machines have achieved a broad and growing set of linguistic competencies,
thanks to recent progress in Natural Language Processing (NLP). Psychologists have …

Methods of deep neural networks (DNNs) have recently demonstrated superior performance
on a number of natural language processing tasks. However, in most previous work, the …

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 …

Efforts to understand the brain bases of language face the Mapping Problem: At what level
do linguistic computations and representations connect to human neurobiology? We review …

Artificial Intelligence federates numerous scientific fields in the aim of developing machines
able to assist human operators performing complex treatments---most of which demand high …