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 …

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 …

We describe the implications of permutation symmetry for the state space and dynamics of
quantum mechanical systems of matrices of general size N. We solve the general 11 …

Abstract Argumentation mining is an advanced form of human language understanding by
the machine. This is a challenging task for a machine. When sufficient explicit discourse …

A bstract Permutation invariant polynomial functions of matrices have previously been
studied as the observables in matrix models invariant under SN, the symmetric group of all …

In both quantum mechanics and corpus linguistics based on vector spaces, the notion of
entanglement provides a means for the various subsystems to communicate with each other …

Distributional semantic models provide vector representations for words by gathering co-
occurrence frequencies from corpora of text. Compositional distributional models extend …

A common problem in cognitive modelling is lack of access to accurate broad-coverage
models of event-level surprisal. As shown in, eg, Bicknell et al.(2010), event-level …

Vector models of language are based on the contextual aspects of language, the
distributions of words and how they co-occur in text. Truth conditional models focus on the …

Recent research in computational linguistics has developed algorithms which associate
matrices with adjectives and verbs, based on the distribution of words in a corpus of text …