The meeting between Natural Language Processing (NLP) and Quantum Computing has
been very successful in recent years, leading to the development of several approaches of …

Many existing deep learning models for natural language processing tasks focus on
learning the compositionality of their inputs, which requires many expensive computations …

The ability to accurately represent sentences is central to language understanding. We
describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network …

We provide conceptual and mathematical foundations for near-term quantum natural
language processing (QNLP), and do so in quantum computer scientist friendly terms. We …

Natural language processing (NLP) is at the forefront of great advances in contemporary AI,
and it is arguably one of the most challenging areas of the field. At the same time, with the …

Semantics is a research field that has gained an extensive interest recently. This survey
describes recent works in the field of semantics, a part of the broader area of computational …

This paper addresses the problem of mapping natural language text to knowledge base
entities. The mapping process is approached as a composition of a phrase or a sentence …

We provide a comparative study between neural word representations and traditional vector
spaces based on co-occurrence counts, in a number of compositional tasks. We use three …

We propose a new application of quantum computing to the field of natural language
processing. Ongoing work in this field attempts to incorporate grammatical structure into …

Deep compositional models of meaning acting on distributional representations of words in
order to produce vectors of larger text constituents are evolving to a popular area of NLP …