Computers understand very little of the meaning of human language. This profoundly limits
our ability to give instructions to computers, the ability of computers to explain their actions to …

Distributional models represent a word through the contexts in which it has been observed.
They can be used to predict similarity in meaning, based on the distributional hypothesis …

This paper presents a training method that encodes each word into a different vector in
semantic space and its relation to low entropy coding. Elman network is employed in the …

Much of our understanding of human thinking is based on probabilistic models. This
innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the …

Distributional semantic models derive computational representations of word meaning from
the patterns of co-occurrence of words in text. Such models have been a success story of …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

Vector‐based models of word meaning have become increasingly popular in cognitive
science. The appeal of these models lies in their ability to represent meaning simply by …

Research into corpus-based semantics has focused on the development of ad hoc models
that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled …

The idea that at least some aspects of word meaning can be induced from patterns of word
co-occurrence is becoming increasingly popular. However, there is less agreement about …

Word embeddings resulting from neural language models have been shown to be
successful for a large variety of NLP tasks. However, such architecture might be difficult to …