We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).
We introduce diagrammatic differentiation for tensor calculus by generalising the dual number construction from rigs to monoidal categories.
We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar.
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.
We present some categorical investigations into Wittgenstein's language-games, with applications to game-theoretic pragmatics and question-answering in natural language processing.
In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP.
Categorical compositional distributional semantics provide a method to derive the meaning of a sentence from the meaning of its individual words: the grammatical reduction of a sentence automatically induces a linear map for composing the word vectors obtained from distributional semantics.