Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

Prior work decoding linguistic meaning from imaging data has been largely limited to
concrete nouns, using similar stimuli for training and testing, from a relatively small number …

Owing to the need for a deep understanding of linguistic items, semantic representation is
considered to be one of the fundamental components of several applications in Natural …

One major deficiency of most semantic representation techniques is that they usually model
a word type as a single point in the semantic space, hence conflating all the meanings that …

Quantifying semantic similarity between linguistic items lies at the core of many applications
in Natural Language Processing and Artificial Intelligence. It has therefore received a …

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 …

This article presents a new model for word sense disambiguation formulated in terms of
evolutionary game theory, where each word to be disambiguated is represented as a node …

Emoji have grown to become one of the most important forms of communication on the web.
With its widespread use, measuring the similarity of emoji has become an important problem …

A large body of research into semantic textual similarity has focused on constructing state-of-
the-art embeddings using sophisticated modelling, careful choice of learning signals and …

Semantic networks and semantic spaces have been two prominent approaches to represent
lexical semantics. While a unified account of the lexical meaning relies on one being able to …