A Study of Entanglement in a Categorical Framework of Natural Language

Abstract

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. In this paper we examine a number of implementations of the categorical framework of Coecke, Sadrzadeh and Clark (2010) for natural language, from an entanglement perspective. Specifically, our goal is to better understand in what way the level of entanglement of the relational tensors (or the lack of it) affects the compositional structures in practical situations. Our findings reveal that a number of proposals for verb construction lead to almost separable tensors, a fact that considerably simplifies the interactions between the words. We examine the ramifications of this fact, and we show that the use of Frobenius algebras mitigates the potential problems to a great extent. Finally, we briefly examine a machine learning method that creates verb tensors exhibiting a sufficient level of entanglement.

In Proceedings QPL 2014, arXiv:1412.8102

References in corpus (3)

• The Frobenius anatomy of word meanings I: subject and object relative pronouns
• Experimenting with Transitive Verbs in a DisCoCat
• The Frobenius anatomy of word meanings II: possessive relative pronouns

Cited by in corpus (8)

• Foundations for Near-Term Quantum Natural Language Processing
• Evaluating Neural Word Representations in Tensor-Based Compositional Settings
• Using Sentence Plausibility to Learn the Semantics of Transitive Verbs
• Meaning updating of density matrices
• Distributional Inclusion Hypothesis for Tensor-based Composition
• Talking Space: inference from spatial linguistic meanings
• A Factorized Model for Transitive Verbs in Compositional Distributional Semantics
• One Representation per Word - Does it make Sense for Composition?