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

    This model for morphosyntactic annotation of non-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), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1794) and the hr500k training corpus (http://hdl.handle.net/11356/1792), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1789). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~92.64. The difference to the previous version of the model is that this version uses the new version of Serbian word embeddings and is trained on a combination of three training corpora (SETimes.SR, ReLDI-NormTagNER-sr, hr500k).

  • DG-POLFIE: POLFIE and Malt-based syntactic parser

    DG-POLFIE is a prototypical parser that tries to merge parse fragments generated by POLFIE using Polish Dependency Parser DG-POLFIE aims to improve the coverage of the POLFIE parser (i.e. the percentage of sentences with at least one analysis). In order to increase the number of Polish sentences and constructions that could be parsed with the POLFIE-based parser, DG-POLFIE defines some rules that use depenency structure to build full parse from the FRAGMENTS provided by POLFIE.

  • Saper

    Shallow semantic parser for polish texts processing. Contains word sense disambiguation, mapping go SUMO concepts and semantic role labelling.

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

    This model for named entity recognition of non-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), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed.

  • The CLASSLA-Stanza model for UD dependency parsing of standard Slovenian 2.2

    This model for UD dependency parsing 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/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated LAS of the parser is ~90.42. The difference to the previous version of the model is that the model was trained using the improved SUK 1.1 version of the training corpus.

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

    This model for morphosyntactic annotation of non-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/11356/1747) and the Janes-Tag corpus (http://hdl.handle.net/11356/1732), using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) that were expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~92.17. The difference to the previous version of the model is that the model was trained on the SUK training corpus and the 3.0 version of Janes-tag, uses new embeddings and the new version of the Slovene morphological lexicon Sloleks 3.0 (http://hdl.handle.net/11356/1745).

  • The CLASSLA-Stanza model for morphosyntactic annotation of standard Slovenian 2.0

    This model for morphosyntactic annotation 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/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) that were expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~98.27. The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses new embeddings and the new version of the Slovene morphological lexicon Sloleks 3.0 (http://hdl.handle.net/11356/1745).

  • Multilabel Error Classifier (Icelandic Error Corpus categories) for Sentences (22.01)

    The Icelandic Error Corpus (IEC) was used to fine tune the Icelandic language model IceBERT for sentence classification. The objective was to train grammatical error detection models that could classify whether a sentence contains a particular error type. The model can mark sentences as including one or more of the following issues: coherence, grammar, orthography, other, style and vocabulary. The overall F1 score is a modest 64%. --- Íslenska villumálheildin (IEC) var notuð til að fínþjálfa íslenska mállíkanið IceBERT fyrir flokkun á setningum. Markmiðið var að þjálfa líkan sem getur greint hvort setning innihaldi ákveðna villutegund. Líkanið getur merkt við setningar með einum eða fleiri mörkum af eftirfarandi: coherence, grammar, orthography, other, style og vocabulary. F1 yfir heildina er 64%.

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

    This model for named entity recognition of non-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), the hr500k training corpus (http://hdl.handle.net/11356/1183), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240) and the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed.