Quantum projections on conceptual subspaces

One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is...

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Autores: Jorge Botana, Guillermo de, Olmos Albacete, Ricardo, Martínez Huertas, José Ángel, Martínez Mingo, Alejandro
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/12612
Acceso en línea:https://hdl.handle.net/20.500.14468/12612
Access Level:acceso abierto
Palabra clave:Quantum similarity model
Semantic-vector space models
Computational linguistics
Similarity
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oai_identifier_str oai:e-spacio.uned.es:20.500.14468/12612
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repository_id_str
spelling Quantum projections on conceptual subspacesJorge Botana, Guillermo deOlmos Albacete, RicardoMartínez Huertas, José ÁngelMartínez Mingo, AlejandroQuantum similarity modelSemantic-vector space modelsComputational linguisticsSimilarityOne of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky’s classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces.Elseviere-Spacio UNED20242024-05-2020232023-12-0120232023-12-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/12612reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0oai:e-spacio.uned.es:20.500.14468/126122026-06-06T12:38:31Z
dc.title.none.fl_str_mv Quantum projections on conceptual subspaces
title Quantum projections on conceptual subspaces
spellingShingle Quantum projections on conceptual subspaces
Jorge Botana, Guillermo de
Quantum similarity model
Semantic-vector space models
Computational linguistics
Similarity
title_short Quantum projections on conceptual subspaces
title_full Quantum projections on conceptual subspaces
title_fullStr Quantum projections on conceptual subspaces
title_full_unstemmed Quantum projections on conceptual subspaces
title_sort Quantum projections on conceptual subspaces
dc.creator.none.fl_str_mv Jorge Botana, Guillermo de
Olmos Albacete, Ricardo
Martínez Huertas, José Ángel
Martínez Mingo, Alejandro
author Jorge Botana, Guillermo de
author_facet Jorge Botana, Guillermo de
Olmos Albacete, Ricardo
Martínez Huertas, José Ángel
Martínez Mingo, Alejandro
author_role author
author2 Olmos Albacete, Ricardo
Martínez Huertas, José Ángel
Martínez Mingo, Alejandro
author2_role author
author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv Quantum similarity model
Semantic-vector space models
Computational linguistics
Similarity
topic Quantum similarity model
Semantic-vector space models
Computational linguistics
Similarity
description One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky’s classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-12-01
2023
2023-12-01
2024
2024-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/12612
url https://hdl.handle.net/20.500.14468/12612
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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