Abstract Quantum Natural Language Processing (QNLP) deals with the design and
implementation of NLP models intended to be run on quantum hardware. In this paper, we …

Deep Neural Network (DNN) models have been shown to have high empirical privacy
leakages. Clinical language models (CLMs) trained on clinical data have been used to …

In this work, we describe a full-stack pipeline for natural language processing on near-term
quantum computers, aka QNLP. The language-modelling framework we employ is that of …

In the decade since 2010, successes in artificial intelligence have been at the forefront of
computer science and technology, and vector space models have solidified a position at the …

False information detection on social media is challenging as it commonly requires tedious
evidence-collecting but lacks available comparative information. Clues mined from user …

Distributional semantic models have become a mainstay in NLP, providing useful features
for downstream tasks. However, assessing long-term progress requires explicit long-term …

The categorical compositional distributional model of natural language provides a
conceptually motivated procedure to compute the meaning of a sentence, given its …

Words can have multiple senses. Compositional distributional models of meaning have
been argued to deal well with finer shades of meaning variation known as polysemy, but are …

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

The categorical compositional distributional model of meaning gives the composition of
words into phrases and sentences pride of place. However, it has so far lacked a model of …