The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI.
However, when it comes to Natural Language Processing (NLP), symbols have to be …

Devising automatic tools to assist specialists in the early detection of mental disturbances
and psychotic disorders is to date a challenging scientific problem and a practically relevant …

Distributional semantics based on neural approaches is a cornerstone of Natural Language
Processing, with surprising connections to human meaning representation as well. Recent …

Introduction Automatic linguistic analysis can provide cost-effective, valuable clues to the
diagnosis of cognitive difficulties and to therapeutic practice, and hence impact positively on …

Concreteness is a fundamental dimension of word semantic representation that has
attracted more and more interest to become one of the most studied variables in the …

We present a word-sense induction method based on pre-trained masked language models
(MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a …

Contextualised word embeddings generated from Neural Language Models (NLMs), such
as BERT, represent a word with a vector that considers the semantics of the target word as …

In the last years many efforts have been spent to build word embeddings, a representational
device in which word meanings are described through dense unit vectors of real numbers …

Social-media platforms have become a global phenomenon of communication, where users
publish content in text, images, video, audio or a combination of them to convey opinions …

Meaning of words constantly changes given the events in modern civilization. Large
Language Models use word embeddings, which are often static and thus cannot cope with …