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Towards an evolutionary-based approach for natural language processing

2020-06-25Documento de conferência Acesso aberto
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dc.contributor.authorManzoni, Luca
dc.contributor.authorJakobovic, Domagoj
dc.contributor.authorMariot, Luca
dc.contributor.authorPicek, Stjepan
dc.contributor.authorCastelli, Mauro
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2020-10-16T22:18:18Z
dc.date.available2020-10-16T22:18:18Z
dc.date.issued2020-06-25
dc.descriptionManzoni, L., Jakobovic, D., Mariot, L., Picek, S., & Castelli, M. (2020). Towards an evolutionary-based approach for natural language processing. In GECCO 2020: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 985-993). (GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/3377930.3390248
dc.description.abstractTasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a first proof-of-concept that combines GP with the well established NLP tool word2vec for the next word prediction task. The main idea is that, once words have been moved into a vector space, traditional GP operators can successfully work on vectors, thus producing meaningful words as the output. To assess the suitability of this approach, we perform an experimental evaluation on a set of existing newspaper headlines. Individuals resulting from this (pre-)training phase can be employed as the initial population in other NLP tasks, like sentence generation, which will be the focus of future investigations, possibly employing adversarial co-evolutionary approaches.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent9
dc.format.extent1521139
dc.identifier.doi10.1145/3377930.3390248
dc.identifier.isbn9781450371285
dc.identifier.otherPURE: 20290837
dc.identifier.otherPURE UUID: 59c3ceaa-20a4-4b53-86de-6c68b793ac84
dc.identifier.otherScopus: 85091765216
dc.identifier.otherORCID: /0000-0002-8793-1451/work/81859095
dc.identifier.otherWOS: 000605292300114
dc.identifier.urihttp://hdl.handle.net/10362/105724
dc.identifier.urlhttps://www.scopus.com/pages/publications/85091765216
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000605292300114
dc.language.isoeng
dc.peerreviewedyes
dc.publisherACM - Association for Computing Machinery
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
dc.relationData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
dc.subjectGenetic programming
dc.subjectNatural language processing
dc.subjectNext word prediction
dc.subjectArtificial Intelligence
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.titleTowards an evolutionary-based approach for natural language processingen
dc.typeconference object
degois.publication.firstPage985
degois.publication.lastPage993
degois.publication.titleGECCO 2020
degois.publication.title2020 Genetic and Evolutionary Computation Conference, GECCO 2020
dspace.entity.typePublication
oaire.awardNumberDSAIPA/DS/0113/2019
oaire.awardNumberDSAIPA/DS/0022/2018
oaire.awardTitleData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0113%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicatione750f897-cfb5-46b7-84ff-3b768eeb44f6
relation.isProjectOfPublicationc35c919f-29eb-4019-b809-622c143b6c56
relation.isProjectOfPublication.latestForDiscoverye750f897-cfb5-46b7-84ff-3b768eeb44f6

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