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NLPyPort: Named Entity Recognition with CRF and Rule-Based Relation Extraction

dc.contributor.authorFerreira, João
dc.contributor.authorOliveira, Hugo Gonçalo
dc.contributor.authorRodrigues, Ricardo
dc.date.accessioned2026-01-26T17:06:12Z
dc.date.available2026-01-26T17:06:12Z
dc.date.issued2019
dc.description.abstractThis paper describes the application of the NLPyPort pipeline to Named Entity Recognition (NER) and Relation Extraction in Portuguese, more precisely in the scope of the IberLEF-2019 evaluation task on the topic. NER was tackled with CRF, based on several features, and trained in the HAREM collection, but results were low. This was partly caused by an issue on the submitted model, which had been trained in lowercase text, but, apparently, also due to the training data used, which highlights the different natures of HAREM, the source of the majority of the testing corpus, and SIGARRA. Relations were extracted with a set of rules bootstrapped from the examples provided by the organisation. Despite an F1-score of 0.72, we were the only participants in this task. We also express our doubts concerning the utility of the extracted relations.eng
dc.identifier.urihttp://hdl.handle.net/10400.26/61202
dc.language.isoeng
dc.peerreviewedn/a
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNLP
dc.subjectNER
dc.subjectCRF
dc.subjectRelation Extraction
dc.subjectPoS Tagging
dc.subjectPattern Based
dc.titleNLPyPort: Named Entity Recognition with CRF and Rule-Based Relation Extractioneng
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferenceDate2019
oaire.citation.endPage476
oaire.citation.startPage468
oaire.citation.titleProceedings of the Iberian Languages Evaluation Forum (IberLEF 2019)
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRodrigues
person.givenNameRicardo
person.identifier.ciencia-idD31C-FB4A-FEAA
person.identifier.orcid0000-0002-6262-7920
relation.isAuthorOfPublicationc64ccf7c-eca2-43cf-a4a2-78e684499c00
relation.isAuthorOfPublication.latestForDiscoveryc64ccf7c-eca2-43cf-a4a2-78e684499c00

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