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  • EpiLexO

    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.

  • CombiTagger

    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.

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

    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.

  • Grafon

    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

  • Liner2.6 model NER NKJP

    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/

  • Universal Dependencies 2.3 Models for UDPipe (2018-11-15)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 84 treebanks of 56 languages of Universal Depenencies 2.3 Treebanks, created solely using UD 2.3 data (http://hdl.handle.net/11234/1-2895). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_23_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

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

    This model for named entity recognition 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 CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206).