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Found 49 papers, 7 papers with code
A toy distributional model for fuzzy generalised quantifiers
It is possible to overcome the computational hurdles by working with fuzzy generalised quantifiers.
Developments in Sheaf-Theoretic Models of Natural Language Ambiguities
Then, we show how an extension of the natural language processing challenge, known as the Winograd Schema, which involves anaphoric ambiguities can be modelled on the Bell-CHSH scenario with a contextual fraction of 0. 096.
Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach
Previous work extended the QNLP translation to discourse structure using points in a closure of Hilbert spaces.
Generalised Winograd Schema and its Contextuality
In this work, we focus on coreference ambiguities and investigate the Winograd Schema Challenge (WSC), a test proposed by Levesque in 2011 to evaluate the intelligence of machines.
Proceedings Modalities in substructural logics: Applications at the interfaces of logic, language and computation
By calling into question the implicit structural rules that are taken for granted in classical logic, substructural logics have brought to the fore new forms of reasoning with applications in many interdisciplinary areas of interest.
A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT
Ambiguities of natural language do not preclude us from using it and context helps in getting ideas across.
A Quantum Natural Language Processing Approach to Pronoun Resolution
We use the Lambek Calculus with soft sub-exponential modalities to model and reason about discourse relations such as anaphora and ellipsis.
The Causal Structure of Semantic Ambiguities
Ambiguity is a natural language phenomenon occurring at different levels of syntax, semantics, and pragmatics.
Permutation invariant matrix statistics and computational language tasks
The Linguistic Matrix Theory programme introduced by Kartsaklis, Ramgoolam and Sadrzadeh is an approach to the statistics of matrices that are generated in type-driven distributional semantics, based on permutation invariant polynomial functions which are regarded as the key observables encoding the significant statistics.
Vector Space Semantics for Lambek Calculus with Soft Subexponentials
We develop a vector space semantics for Lambek Calculus with Soft Subexponentials, apply the calculus to construct compositional vector interpretations for parasitic gap noun phrases and discourse units with anaphora and ellipsis, and experiment with the constructions in a distributional sentence similarity task.
Fuzzy Generalised Quantifiers for Natural Language in Categorical Compositional Distributional Semantics
We show that this category is a concrete instantiation of the compositional distributional model.
Pregroup Grammars, their Syntax and Semantics
Pregroup grammars were developed in 1999 and stayed Lambek's preferred algebraic model of grammar.
On the Quantum-like Contextuality of Ambiguous Phrases
Language is contextual as meanings of words are dependent on their contexts.
Representation Learning for Type-Driven Composition
This paper is about learning word representations using grammatical type information.
Cosine Similarity of Multimodal Content Vectors for TV Programmes
Multimodal information originates from a variety of sources: audiovisual files, textual descriptions, and metadata.
A Frobenius Algebraic Analysis for Parasitic Gaps
The interpretation of parasitic gaps is an ostensible case of non-linearity in natural language composition.
Categorical Vector Space Semantics for Lambek Calculus with a Relevant Modality
We develop a categorical compositional distributional semantics for Lambek Calculus with a Relevant Modality ! L*, which has a limited edition of the contraction and permutation rules.
Incremental Monoidal Grammars
In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata.
Gaussianity and typicality in matrix distributional semantics
Using the general 13-parameter permutation invariant Gaussian matrix models recently solved, we find, using a dataset of matrices constructed via standard techniques in distributional semantics, that the expectation values of a large class of cubic and quartic observables show high gaussianity at levels between 90 to 99 percent.
Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings
Our results show that non-linear addition and a non-linear tensor-based composition outperform the naive non-compositional baselines and the linear models, and that sentence encoders perform well on sentence similarity, but not on verb disambiguation.
A Typedriven Vector Semantics for Ellipsis with Anaphora using Lambek Calculus with Limited Contraction
We review previous compositional distributional models of relative pronouns, coordination and a restricted account of ellipsis in the DisCoCat framework of Coecke et al. (2010, 2013).
Classical Copying versus Quantum Entanglement in Natural Language: The Case of VP-ellipsis
This paper compares classical copying and quantum entanglement in natural language by considering the case of verb phrase (VP) ellipsis.
Exploring Semantic Incrementality with Dynamic Syntax and Vector Space Semantics
One of the fundamental requirements for models of semantic processing in dialogue is incrementality: a model must reflect how people interpret and generate language at least on a word-by-word basis, and handle phenomena such as fragments, incomplete and jointly-produced utterances.
Static and Dynamic Vector Semantics for Lambda Calculus Models of Natural Language
Vector models of language are based on the contextual aspects of language, the distributions of words and how they co-occur in text.
Linguistic Matrix Theory
We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations.
Compositional Distributional Models of Meaning
Compositional distributional models of meaning (CDMs) provide a function that produces a vectorial representation for a phrase or a sentence by composing the vectors of its words.
Distributional Inclusion Hypothesis for Tensor-based Composition
According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributional vectors.
Quantifier Scope in Categorical Compositional Distributional Semantics
In previous work with J. Hedges, we formalised a generalised quantifiers theory of natural language in categorical compositional distributional semantics with the help of bialgebras.
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.
Sentence Entailment in Compositional Distributional Semantics
In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein.
Distributional Sentence Entailment Using Density Matrices
Categorical compositional distributional model of Coecke et al. (2010) suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics.
A Frobenius Model of Information Structure in Categorical Compositional Distributional Semantics
The categorical compositional distributional model of Coecke, Sadrzadeh and Clark provides a linguistically motivated procedure for computing the meaning of a sentence as a function of the distributional meaning of the words therein.
Open System Categorical Quantum Semantics in Natural Language Processing
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.
Evaluating Neural Word Representations in Tensor-Based Compositional Settings
We provide a comparative study between neural word representations and traditional vector spaces based on co-occurrence counts, in a number of compositional tasks.
Resolving Lexical Ambiguity in Tensor Regression Models of Meaning
This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition.
The Frobenius anatomy of word meanings II: possessive relative pronouns
Within the categorical compositional distributional model of meaning, we provide semantic interpretations for the subject and object roles of the possessive relative pronoun `whose'.
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.
The Frobenius anatomy of word meanings I: subject and object relative pronouns
This paper develops a compositional vector-based semantics of subject and object relative pronouns within a categorical framework.
Semantic Unification A sheaf theoretic approach to natural language
Language is contextual and sheaf theory provides a high level mathematical framework to model contextuality.
Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebras
They also provide semantics for Lambek's pregroup algebras, applied to formalizing the grammatical structure of natural language, and are implicit in a distributional model of word meaning based on vector spaces.
A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure
We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation.
Experimental Support for a Categorical Compositional Distributional Model of Meaning
The evaluation is based on the word disambiguation task developed by Mitchell and Lapata (2008) for intransitive sentences, and on a similar new experiment designed for transitive sentences.
Mathematical Foundations for a Compositional Distributional Model of Meaning
We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for which we rely on the algebra of Pregroups, introduced by Lambek.
