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• The CLASSLA-Stanza model for semantic role labeling of standard Slovenian

  2 resources

  The model for semantic role labeling of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1434). The estimated F1 of the semantic role annotations is ~77.2.

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• VIADAT-ANNOTATE

  2 resources

  A VIADAT module; VIADAT-ANNOTATE is an interactive annotation environment. Developed in cooperation with ÚSD AV ČR and NFA.

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• Liner2.5 model NER

  2 resources

  Przygotował: Michał Marcińczuk <marcinczuk@gmail.com> Data: 25.05.2016 Projekt: Clarin-PL (http://clarin-pl.eu) Autorzy: Michał Marcińczuk, Jan Kocoń, Michał Krautforst Modele do narzędzia Liner2.5 do rozpoznawania jednostek identyfikacyjnych. Narzędzie Liner2.5 dostępne jest pod linkiem http://hdl.handle.net/11321/231. Paczka zawiera trzy modele: 1. config-nam.ini -- granice jednostek identyfikacyjnych, 2. config-top9.ini -- granice i ogólna kategoryzacja jednostek (9 kategorii), 3. config-n82.ini -- granice i szczegółowa kategoryzacja jednostek (82 kategorie).

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• The CLASSLA-StanfordNLP model for lemmatisation of standard Croatian

  2 resources

  The model for lemmatisation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). The estimated F1 of the lemma annotations is ~97.6.

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• The CLASSLA-StanfordNLP model for named entity recognition of standard Serbian 1.0

  3 resources

  This model for named entity recognition of standard Serbian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) and using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206).

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• The CLASSLA-Stanza model for morphosyntactic annotation of standard Bulgarian 2.1

  3 resources

  This model for morphosyntactic annotation of standard Bulgarian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the BulTreeBank training corpus (https://clarino.uib.no/korpuskel/corpora) and using the CLARIN.SI-embed.bg word embeddings (http://hdl.handle.net/11356/1796). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~96.83. The difference to the previous version of the model is that this version was trained using the new version of the Bulgarian word embeddings.

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• The CLASSLA-StanfordNLP model for lemmatisation of standard Bulgarian 1.1

  2 resources

  The model for lemmatisation of standard Bulgarian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the BulTreeBank training corpus (http://hdl.handle.net/11495/D93F-C6E9-65D9-2) and using the Bulgarian inflectional lexicon (Popov, Simov, and Vidinska 1998). The estimated F1 of the lemma annotations is ~98.8. The difference to the previous version of the lemmatizer is that now it relies solely on XPOS annotations, and not on a combination of UPOS, FEATS (lexicon lookup) and XPOS (lemma prediction) annotations.

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• The CLASSLA-Stanza model for named entity recognition of standard Slovenian 2.2

  3 resources

  This model for named entity recognition of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1959) and using the CLARIN.SI-embed.sl 2.0 word embeddings (http://hdl.handle.net/11356/1791). The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses new embeddings.

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• The CLASSLA-Stanza model for morphosyntactic annotation of standard Serbian 2.1

  3 resources

  The model for morphosyntactic annotation of standard Serbian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) combined with the Croatian hr500k training dataset (http://hdl.handle.net/11356/1792) to ensure sufficient representation of certain labels. The CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1789) were used during training. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~96.19. The difference to the previous version of the model is that this version was trained on the SETimes.SR corpus expanded with the Croatian hr500k training dataset to ensure sufficient representation of certain labels. it was also trained using the new version of Serbian word embeddings.

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• Liner2.5-events and event relations

  1 resources

  Liner2.5 configured for the recognition of event attributes and event relations

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