Search results

• 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.

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

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

  2 resources

  The model for lemmatisation 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 Bulgarian inflectional lexicon (Popov, Simov, and Vidinska 1998). The estimated F1 of the lemma annotations is ~98.93. The difference to the previous version of the lemmatizer is that this version was trained using the new version of the Bulgarian word embeddings.

  Use "The CLASSLA-Stanza model for lemmatisation of standard Bulgarian 2.1"

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

  2 resources

  The model for lemmatisation 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) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1794), 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 ~94.92. The difference to the previous version of the model is that this version is trained on a combination of two corpora (SETimes.SR, ReLDI-NormTagNER-sr).

  Use "The CLASSLA-Stanza model for lemmatisation of non-standard Serbian 2.1"

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

  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/11356/1183), 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. 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.

  Use "The CLASSLA-StanfordNLP model for lemmatisation of non-standard Croatian 1.1"

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

  2 resources

  The model for lemmatisation of standard Macedonian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the 1984 training corpus expanded with the Macedonian SETimes corpus (to be published). The estimated F1 of the lemma annotations is ~98.81. The difference from the previous version is that this version was trained using a larger training dataset.

  Use "The CLASSLA-Stanza model for lemmatisation of standard Macedonian 2.1"

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

  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/11356/1183) 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. 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.

  Use "The CLASSLA-StanfordNLP model for lemmatisation of non-standard Serbian 1.1"

• The CLASSLA-StanfordNLP model for lemmatisation of standard Macedonian 1.0

  2 resources

  The model for lemmatisation of standard Macedonian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the 1984 training corpus (to be published). The estimated F1 of the lemma annotations is ~99.1.

  Use "The CLASSLA-StanfordNLP model for lemmatisation of standard Macedonian 1.0"

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

  2 resources

  The model for lemmatisation 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 Serbian non-standard training corpus ReLDI-NormTagNER-sr (http://hdl.handle.net/11356/1794) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~98.02. The difference to the previous version is that this version was trained on a combination of the standard (SETimes.SR) and non-standard (ReLDI-NormTagNER-sr) Serbian training corpora.

  Use "The CLASSLA-Stanza model for lemmatisation of standard Serbian 2.1"

• 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.

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

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

  2 resources

  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.

  Use "The CLASSLA-Stanza model for lemmatisation of standard Croatian 2.1"