128 research outputs found
A Study of Entanglement in a Categorical Framework of Natural Language
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.Comment: In Proceedings QPL 2014, arXiv:1412.810
A Corpus-based Toy Model for DisCoCat
The categorical compositional distributional (DisCoCat) model of meaning
rigorously connects distributional semantics and pregroup grammars, and has
found a variety of applications in computational linguistics. From a more
abstract standpoint, the DisCoCat paradigm predicates the construction of a
mapping from syntax to categorical semantics. In this work we present a
concrete construction of one such mapping, from a toy model of syntax for
corpora annotated with constituent structure trees, to categorical semantics
taking place in a category of free R-semimodules over an involutive commutative
semiring R.Comment: In Proceedings SLPCS 2016, arXiv:1608.0101
A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras
Categorical compositional distributional semantics is a model of natural
language; it combines the statistical vector space models of words with the
compositional models of grammar. We formalise in this model the generalised
quantifier theory of natural language, due to Barwise and Cooper. The
underlying setting is a compact closed category with bialgebras. We start from
a generative grammar formalisation and develop an abstract categorical
compositional semantics for it, then instantiate the abstract setting to sets
and relations and to finite dimensional vector spaces and linear maps. We prove
the equivalence of the relational instantiation to the truth theoretic
semantics of generalised quantifiers. The vector space instantiation formalises
the statistical usages of words and enables us to, for the first time, reason
about quantified phrases and sentences compositionally in distributional
semantics
A Proof-Theoretic Approach to Scope Ambiguity in Compositional Vector Space Models
We investigate the extent to which compositional vector space models can be
used to account for scope ambiguity in quantified sentences (of the form "Every
man loves some woman"). Such sentences containing two quantifiers introduce two
readings, a direct scope reading and an inverse scope reading. This ambiguity
has been treated in a vector space model using bialgebras by (Hedges and
Sadrzadeh, 2016) and (Sadrzadeh, 2016), though without an explanation of the
mechanism by which the ambiguity arises. We combine a polarised focussed
sequent calculus for the non-associative Lambek calculus NL, as described in
(Moortgat and Moot, 2011), with the vector based approach to quantifier scope
ambiguity. In particular, we establish a procedure for obtaining a vector space
model for quantifier scope ambiguity in a derivational way.Comment: This is a preprint of a paper to appear in: Journal of Language
Modelling, 201
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