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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn978-3-030-15719-7pt_BR
dc.contributor.authorunicampRocha, Anderson de Rezende-
dc.typeArtigopt_BR
dc.titleOpen-set web genre identification using distributional features and nearest neighbors distance ratiopt_BR
dc.contributor.authorPritsos, Dimitrios-
dc.contributor.authorRocha, Anderson-
dc.contributor.authorStamatatos, Efstathios-
dc.subjectGêneropt_BR
dc.subjectAlgoritmospt_BR
dc.subject.otherlanguageGenderpt_BR
dc.subject.otherlanguageAlgorithmspt_BR
dc.description.abstractWeb genre identification can boost information retrieval systems by providing rich descriptions of documents and enabling more specialized queries. The open-set scenario is more realistic for this task as web genres evolve over time and it is not feasible to define a universally agreed genre palette. In this work, we bring to bear a novel approach to web genre identification underpinned by distributional features acquired by doc2vec and a recently-proposed open-set classification algorithm—the nearest neighbors distance ratio classifier. We present experimental results using a benchmark corpus and a strong baseline and demonstrate that the proposed approach is highly competitive, especially when emphasis is given on precisionpt_BR
dc.relation.ispartofLecture notes in computer sciencept_BR
dc.relation.ispartofabbreviationLect. notes comput. sci.pt_BR
dc.publisher.cityBerlimpt_BR
dc.publisher.countryAlemanhapt_BR
dc.publisherSpringerpt_BR
dc.date.issued2019-
dc.date.monthofcirculationApr.pt_BR
dc.language.isoengpt_BR
dc.description.volume11438pt_BR
dc.description.firstpage3pt_BR
dc.description.lastpage11pt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn0302-9743pt_BR
dc.identifier.eissn1611-3349pt_BR
dc.identifier.doi10.1007/978-3-030-15719-7_1pt_BR
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-030-15719-7_1pt_BR
dc.date.available2020-05-15T15:56:57Z-
dc.date.accessioned2020-05-15T15:56:57Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-05-15T15:56:57Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:17:56Z : No. of bitstreams: 1 2-s2.0-85064856679.pdf: 369979 bytes, checksum: 67e99302f016e6c77f2de163916b1044 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-15T15:56:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/341518-
dc.description.conferencenomeSAMBA : SIPAIM – Miccai biomedical workshop, biomedical information processing and analysis - a Latin American perspectivept_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordOpen-set classificationpt_BR
dc.subject.keywordDistributional featurespt_BR
dc.identifier.source2-s2.0-85064856679pt_BR
dc.creator.orcid0000-0002-4236-8212pt_BR
dc.type.formArtigo de eventopt_BR
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