Search results

• EvaLatin 2020 models for UDPipe 2 (2020-08-31)

  2 resources

  POS Tagger and Lemmatizer models for EvaLatin2020 data (https://github.com/CIRCSE/LT4HALA). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#evalatin20_models . To use these models, you need UDPipe version at least 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• The CLASSLA-StanfordNLP model for lemmatisation of non-standard Croatian 1.0

  2 resources

  The model for lemmatisation 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/11356/1210), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). 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 ~97.54.

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• The CLASSLA-StanfordNLP model for lemmatisation of non-standard Serbian 1.0

  2 resources

  The model for lemmatisation 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 ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the hr500k training corpus (http://hdl.handle.net/11356/1210) and the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/), using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). 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 ~97.62.

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• WebLicht Lemmas DE

  1 resources

  WebLicht Easy Chain for Lemmatization (German). The pipeline makes use of WebLicht's TCF converter, the IMS tokenizer, and the IMS TreeTagger. WebLicht's Tundra can be used to visualize the result.

• DARIAH DKPro-Wrapper: POS-Tagging und Lemmatization EN

  1 resources

  The DARIAH DKPro Wrapper is a wrapper for DKPro Core, a tool for linguistic annotation.

• WebLicht Lemmas EN

  1 resources

  WebLicht Easy Chain for Lemmatization (English). The pipeline makes use of WebLicht's TCF converter, the Stanford tokenizer, the Jitar POS Tagger, and the lemmatizer service from MorphAdorner. WebLicht's Tundra can be used to visualize the result.

• int-pie

  2 resources

  The PIE tagger with custom modifications by the Dutch Language Institute (INT).

• Glem

  1 resources

  GLEM is a lemmatizer for Ancient Greek.

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• Frog

  1 resources

  Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. It performs automatic linguistic enrichment such as part of speech tagging, lemmatisation, named entity recognition, shallow parsing, dependency parsing and morphological analysis. All NLP modules are based on TiMBL.

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• GaLAHaD

  2 resources

  GaLAHaD (Generating Linguistic Annotations for Historical Dutch) allows linguists to compare taggers, tag their own corpora, evaluate the results and export their tagged documents.

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