Search results

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

  2 resources

  The model for lemmatisation 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 srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~97.9. The difference to the previous version of the model is that it is trained with the lemmatiser padding bug removed, cf. https://github.com/stanfordnlp/stanfordnlp/issues/143.

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• EpiLexO

  2 resources

  EpiLexO is a user friendly web application for the creation and editing of an integrated system of language resources for ancient fragmentary languages centered on the lexicon, in compliance with current digital humanities and Linked Open Data principles. EpiLexo allows for the editing of lexica with all relevant cross-references: for their linking to their testimonies, as well as to bibliographic information and other (external) resources and common vocabularies. This front-end application rests on a Service-Oriented Architecture with two main back-end components, the LexO-server (\handle) and the CASH-server (1github), which manage lexica and textual documents respectively via Rest-ful APIs web-services, plus additional services for the management of other aspects such as access and authentication, XML rendering, etc. All code is available on https://github.com/DigItAnt/ The application has been developed in the context of a project on the languages of fragmentary attestation of ancient Italy, but can be applied to other similar contexts.

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• The CLASSLA-Stanza model for morphosyntactic annotation of spoken Slovenian 2.2

  3 resources

  This model for morphosyntactic annotation of spoken Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SST treebank of spoken Slovenian (https://github.com/UniversalDependencies/UD_Slovenian-SST) combined with the SUK training corpus (http://hdl.handle.net/11356/1959) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1791) 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 ~96.76.

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• KPWr n82 NER model (on Polish RoBERTa base)

  2 resources

  The named entity recognition model for fine-grained categories of entities (82 types) was trained on the KPWr corpus using Polish RoBERTa base language model. Details can be found on the following page: https://github.com/mczuk/xlm-roberta-ner

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• Grafon

  1 resources

  Representation of sentence semantic with deepened semantic graphs. Graphs are composed based on the output of saper tool https://clarin-pl.eu/dspace/handle/11321/278

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• SlopeQ for BNC Search Engine

  2 resources

  The SlopeQ for BNC Search Engine provides access to the British National Corpus dataset. In addition to linguistically motivated corpus queries, it supports a number of data exploration and visualisation features. Most of the functionality of the search engine is available through a REST web service.

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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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• 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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• CombiTagger

  3 resources

  The main purpose of CombiTagger is to read datafiles generated by individual taggers and use them to develop a combined tagger according to a specified algorithm. The system provides algorithms for simple and weighted voting, but it is extensible so that other combination algorithms can be added easily CombiTagger is implemented in Java.

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• Liner2.6 model NER NKJP

  3 resources

  Liner2.6 NER NKJP model The package contains a pre-trained Liner2 (https://github.com/CLARIN-PL/Liner2) model for recognition named entities according to NKJP guidelines. The model was trained on the NKJP corpus (http://nkjp.pl/) and evaluated in the PolEval 2018 Task 2 (http://poleval.pl/tasks/). The model won third place with the following results: Exact — 0.778, Overlap — 0.818, Final — 0.810. References: * NKJP corpus in TEI format — http://clip.ipipan.waw.pl/NationalCorpusOfPolish?action=AttachFile&do=view&target=NKJP-PodkorpusMilionowy-1.2.tar.gz * PolEval 2018 Task 2 evaluation corpus — http://mozart.ipipan.waw.pl/~axw/poleval2018/

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