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dc.contributor.author | Periñán-Pascual, Carlos | es_ES |
dc.date.accessioned | 2022-10-27T07:30:52Z | |
dc.date.available | 2022-10-27T07:30:52Z | |
dc.date.issued | 2022-03 | es_ES |
dc.identifier.issn | 0269-2821 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/188802 | |
dc.description.abstract | [EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients. | es_ES |
dc.description.sponsorship | s Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer-Verlag | es_ES |
dc.relation.ispartof | Artificial Intelligence Review | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Association measure | es_ES |
dc.subject | Neural network | es_ES |
dc.subject | Word embedding | es_ES |
dc.subject | Word2Vec | es_ES |
dc.subject | GloVe | es_ES |
dc.subject | FastText | es_ES |
dc.subject.classification | FILOLOGIA INGLESA | es_ES |
dc.title | Measuring associational thinking through word embeddings | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s10462-021-10056-6 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112827GB-I00/ES/SISTEMA INTELIGENTE MULTIMODAL BASADO EN CROWDSENSING PARA UN SERVICIO DE PREDICCION DE PROBLEMAS SOCIALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC-2017-6389-5-AR//PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IOT/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/101017861/EU | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Lingüística Aplicada - Departament de Lingüística Aplicada | es_ES |
dc.description.bibliographicCitation | Periñán-Pascual, C. (2022). Measuring associational thinking through word embeddings. Artificial Intelligence Review. 55(3):2065-2102. https://doi.org/10.1007/s10462-021-10056-6 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s10462-021-10056-6 | es_ES |
dc.description.upvformatpinicio | 2065 | es_ES |
dc.description.upvformatpfin | 2102 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 55 | es_ES |
dc.description.issue | 3 | es_ES |
dc.relation.pasarela | S\456465 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | COMISION DE LAS COMUNIDADES EUROPEA | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
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