Generalized relations in linguistics and cognition

Categorical compositional models of natural language exploit grammatical structure to calculate the meaning of sentences from the meanings of individual words. This approach outperforms conventional techniques for some standard NLP tasks. More recently, similar compositional techniques have been app...

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Bibliographic Details
Main Authors: Coecke, B, Genovese, F, Lewis, M, Marsden, D
Format: Conference item
Published: Springer, Berlin, Heidelberg 2017
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author Coecke, B
Genovese, F
Lewis, M
Marsden, D
author_facet Coecke, B
Genovese, F
Lewis, M
Marsden, D
author_sort Coecke, B
collection OXFORD
description Categorical compositional models of natural language exploit grammatical structure to calculate the meaning of sentences from the meanings of individual words. This approach outperforms conventional techniques for some standard NLP tasks. More recently, similar compositional techniques have been applied to conceptual space models of cognition. Compact closed categories, particularly the category of finite dimensional vector spaces, have been the most common setting for categorical compositional models. When addressing a new problem domain, such as conceptual space models of meaning, a key problem is finding a compact closed category that captures the features of interest. We propose categories of generalized relations as source of new, practical models for cognition and NLP. We demonstrate using detailed examples that phenomena such as fuzziness, metrics, convexity, semantic ambiguity and meaning that varies with context can all be described by relational models. Crucially, by exploiting a technical framework described in previous work of the authors, we also show how we can combine multiple features into a single model, providing a flexible family of new categories for categorical compositional modelling.
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spelling oxford-uuid:d5e24a6e-5c62-4fec-8372-651d33fc4d692022-03-27T08:29:14ZGeneralized relations in linguistics and cognitionConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d5e24a6e-5c62-4fec-8372-651d33fc4d69Symplectic Elements at OxfordSpringer, Berlin, Heidelberg2017Coecke, BGenovese, FLewis, MMarsden, DCategorical compositional models of natural language exploit grammatical structure to calculate the meaning of sentences from the meanings of individual words. This approach outperforms conventional techniques for some standard NLP tasks. More recently, similar compositional techniques have been applied to conceptual space models of cognition. Compact closed categories, particularly the category of finite dimensional vector spaces, have been the most common setting for categorical compositional models. When addressing a new problem domain, such as conceptual space models of meaning, a key problem is finding a compact closed category that captures the features of interest. We propose categories of generalized relations as source of new, practical models for cognition and NLP. We demonstrate using detailed examples that phenomena such as fuzziness, metrics, convexity, semantic ambiguity and meaning that varies with context can all be described by relational models. Crucially, by exploiting a technical framework described in previous work of the authors, we also show how we can combine multiple features into a single model, providing a flexible family of new categories for categorical compositional modelling.
spellingShingle Coecke, B
Genovese, F
Lewis, M
Marsden, D
Generalized relations in linguistics and cognition
title Generalized relations in linguistics and cognition
title_full Generalized relations in linguistics and cognition
title_fullStr Generalized relations in linguistics and cognition
title_full_unstemmed Generalized relations in linguistics and cognition
title_short Generalized relations in linguistics and cognition
title_sort generalized relations in linguistics and cognition
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AT lewism generalizedrelationsinlinguisticsandcognition
AT marsdend generalizedrelationsinlinguisticsandcognition