1,464 research outputs found
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
Types and forgetfulness in categorical linguistics and quantum mechanics
The role of types in categorical models of meaning is investigated. A general
scheme for how typed models of meaning may be used to compare sentences,
regardless of their grammatical structure is described, and a toy example is
used as an illustration. Taking as a starting point the question of whether the
evaluation of such a type system 'loses information', we consider the
parametrized typing associated with connectives from this viewpoint.
The answer to this question implies that, within full categorical models of
meaning, the objects associated with types must exhibit a simple but subtle
categorical property known as self-similarity. We investigate the category
theory behind this, with explicit reference to typed systems, and their
monoidal closed structure. We then demonstrate close connections between such
self-similar structures and dagger Frobenius algebras. In particular, we
demonstrate that the categorical structures implied by the polymorphically
typed connectives give rise to a (lax unitless) form of the special forms of
Frobenius algebras known as classical structures, used heavily in abstract
categorical approaches to quantum mechanics.Comment: 37 pages, 4 figure
Introducing a Calculus of Effects and Handlers for Natural Language Semantics
In compositional model-theoretic semantics, researchers assemble
truth-conditions or other kinds of denotations using the lambda calculus. It
was previously observed that the lambda terms and/or the denotations studied
tend to follow the same pattern: they are instances of a monad. In this paper,
we present an extension of the simply-typed lambda calculus that exploits this
uniformity using the recently discovered technique of effect handlers. We prove
that our calculus exhibits some of the key formal properties of the lambda
calculus and we use it to construct a modular semantics for a small fragment
that involves multiple distinct semantic phenomena
Open System Categorical Quantum Semantics in Natural Language Processing
Originally inspired by categorical quantum mechanics (Abramsky and Coecke, LiCS'04), the categorical compositional distributional model of natural language meaning of Coecke, Sadrzadeh and Clark provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure within a Lambek pregroup and a vectorial representation of the meaning of its parts. The predictions of this first model have outperformed that of other models in mainstream empirical language processing tasks on large scale data. Moreover, just like CQM allows for varying the model in which we interpret quantum axioms, one can also vary the model in which we interpret word meaning. In this paper we show that further developments in categorical quantum mechanics are relevant to natural language processing too. Firstly, Selinger's CPM-construction allows for explicitly taking into account lexical ambiguity and distinguishing between the two inherently different notions of homonymy and polysemy. In terms of the model in which we interpret word meaning, this means a passage from the vector space model to density matrices. Despite this change of model, standard empirical methods for comparing meanings can be easily adopted, which we demonstrate by a small-scale experiment on real-world data. This experiment moreover provides preliminary evidence of the validity of our proposed new model for word meaning. Secondly, commutative classical structures as well as their non-commutative counterparts that arise in the image of the CPM-construction allow for encoding relative pronouns, verbs and adjectives, and finally, iteration of the CPM-construction, something that has no counterpart in the quantum realm, enables one to accommodate both entailment and ambiguity
Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics
This thesis is about the problem of compositionality in distributional
semantics. Distributional semantics presupposes that the meanings of words are
a function of their occurrences in textual contexts. It models words as
distributions over these contexts and represents them as vectors in high
dimensional spaces. The problem of compositionality for such models concerns
itself with how to produce representations for larger units of text by
composing the representations of smaller units of text.
This thesis focuses on a particular approach to this compositionality
problem, namely using the categorical framework developed by Coecke, Sadrzadeh,
and Clark, which combines syntactic analysis formalisms with distributional
semantic representations of meaning to produce syntactically motivated
composition operations. This thesis shows how this approach can be
theoretically extended and practically implemented to produce concrete
compositional distributional models of natural language semantics. It
furthermore demonstrates that such models can perform on par with, or better
than, other competing approaches in the field of natural language processing.
There are three principal contributions to computational linguistics in this
thesis. The first is to extend the DisCoCat framework on the syntactic front
and semantic front, incorporating a number of syntactic analysis formalisms and
providing learning procedures allowing for the generation of concrete
compositional distributional models. The second contribution is to evaluate the
models developed from the procedures presented here, showing that they
outperform other compositional distributional models present in the literature.
The third contribution is to show how using category theory to solve linguistic
problems forms a sound basis for research, illustrated by examples of work on
this topic, that also suggest directions for future research.Comment: DPhil Thesis, University of Oxford, Submitted and accepted in 201
Analysing Ambiguous Nouns and Verbs with Quantum Contextuality Tools
Psycholinguistic research uses eye-tracking to show that polysemous words are disambiguated differently from homonymous words, and that ambiguous verbs are disambiguated differently than ambiguous nouns. Research in Compositional Distributional Semantics uses cosine distances to show that verbs are disambiguated more efficiently in the context of their subjects and objects than when on their own. These two frameworks both focus on one ambiguous word at a time and neither considers ambiguous phrases with two (or more) ambiguous words. We borrow methods and measures from Quantum Information Theory, the framework of Contextuality-by-Default and degrees of contextual influences, and work with ambiguous subject-verb and verb-object phrases of English, where both the subject/object and the verb are ambiguous. We show that differences in the processing of ambiguous verbs versus ambiguous nouns, as well as between different levels of ambiguity in homonymous versus polysemous nouns and verbs can be modelled using the averages of the degrees of their contextual influences
Incremental Composition in Distributional Semantics
Despite the incremental nature of Dynamic Syntax (DS), the semantic
grounding of it remains that of predicate logic, itself grounded in set theory,
so is poorly suited to expressing the rampantly context-relative nature
of word meaning, and related phenomena such as incremental judgements
of similarity needed for the modelling of disambiguation. Here, we show
how DS can be assigned a compositional distributional semantics which
enables such judgements and makes it possible to incrementally disambiguate
language constructs using vector space semantics. Building on a
proposal in our previous work, we implement and evaluate our model on
real data, showing that it outperforms a commonly used additive baseline.
In conclusion, we argue that these results set the ground for an account
of the non-determinism of lexical content, in which the nature of word
meaning is its dependence on surrounding context for its construal
- …