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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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• WiKNN Text Classifier

  2 resources

  WiKNN is an online text classifier service for Polish and English texts. It supports hierarchical labelled classification of user-submitted texts with Wikipedia categories. WiKNN is available through a web-based interface (http://pelcra.clarin-pl.eu/tools/classifier/) and as a REST service with interactive documentation available at http://clarin.pelcra.pl/apidocs/wiknn.

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• Integrated Parser

  2 resources

  Integrated parser is an application that combines and normalizes outputs of several parsers for Polish. It is based on ENIAM processing stream extended with Polish Dependency Parser, Świgra and POLFIE. Particular parsers may turned on and off according to the user requirements.

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• CorPipe 24 Multilingual CorefUD 1.2 Model (corpipe24-corefud1.2-240906)

  2 resources

  The `corpipe24-corefud1.2-240906` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 24 (https://github.com/ufal/crac2024-corpipe). It is released under the CC BY-NC-SA 4.0 license. The model is language agnostic (no corpus id on input), so it can be in theory used to predict coreference in any `mT5` language. This model jointly predicts also the empty nodes needed for zero coreference. The paper introducing this model also presents an alternative two-stage approach first predicting empty nodes (via https://www.kaggle.com/models/ufal-mff/crac2024_zero_nodes_baseline/) and then performing coreference resolution (via http://hdl.handle.net/11234/1-5673), which is circa twice as slow but slightly better.

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