SIGARRA News Corpus NER Models for OpenNLP, Stanford CoreNLP, spaCy, NLTK

Pre-trained models for named entity recognition in Portuguese, using the following entity classes: Hora (Hour), Evento (Event), Organizacao (Organization), Curso (Course), Pessoa (Person), Localizacao (Location), Data (Date) and UnidadeOrganica (Organic Unit).

Data and Resources

open-nlpZIP
NER models for OpenNLP, for the SIGARRA News Corpus, with the best individual...
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stanford-corenlpZIP
NER models for Stanford CoreNLP, for the SIGARRA News Corpus, with the best...
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spacyZIP
NER models for spaCy, for the SIGARRA News Corpus, with the best individual...
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nltkZIP
NER models for NLTK, for the SIGARRA News Corpus, with the best individual...
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Additional Info

Field	Value
Source	https://rdm.inesctec.pt/dataset/cs-2017-004
Author	André Pires
Last Updated	November 15, 2018, 10:38 (UTC)
Created	June 13, 2017, 16:16 (UTC)
dc.Coverage.Spatial	Portugal
dc.Coverage.Temporal	2016-12-14 to 2017-03-01
dc.Date	2017
dc.Format	*.zip
dc.Format.Extent	80MB
dc.Language	PT
dc.Publisher	INESC TEC
dc.Relation	Master´s thesis: PIRES, André (2017).Named entity recognition on Portuguese web text. Porto: Faculdade de Engenharia da Universidade do Porto. http://hdl.handle.net/10216/106094
dc.Type	Serialized Named Entity Recognition Models