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  • WiKNN Text Classifier

    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.

  • Integrated Parser

    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.

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

    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.

  • The CLASSLA-StanfordNLP model for lemmatisation of standard Slovenian 1.3

    The model for lemmatisation of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and using the Sloleks inflectional lexicon (http://hdl.handle.net/11356/1230). The estimated F1 of the lemma annotations is ~99.7. The difference to the previous version is that the internal lexicon is built on the lexicon training data only, and not on the (automatically XPOS-annoteted) corpus data.

  • The CLASSLA-Stanza model for lemmatisation of standard Croatian 2.1

    The model for lemmatisation of standard Croatian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the hr500k training corpus (http://hdl.handle.net/11356/1792) and using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). The estimated F1 of the lemma annotations is ~98.02. The difference to the previous version is that this version was trained on the new version of the hr500k corpus.

  • The CLASSLA-StanfordNLP model for lemmatisation of non-standard Slovenian 1.1

    The model for lemmatisation of non-standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and the Janes-Tag corpus (http://hdl.handle.net/11356/1238), using the Sloleks inflectional lexicon (http://hdl.handle.net/11356/1230). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The estimated F1 of the lemma annotations is ~98.86. 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.

  • 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.