GATE-ANNIE, developed by the GATE group at the University of Sheffield (http;//www.gate.ac.uk; Cunningham et al., 2002,) is an Information Extraction (IE) web service for English. It consists of the following main language processing tools: tokeniser, sentence splitter, POS tagger, coreference resolver and named entity recogniser.
The named entity recogniser identifies and categorizes entity names (such as persons, organizations, and location names), temporal expressions (dates and times), and certain types of numerical expressions (monetary values and percentages).
GATE-ANNIE returns the fully annotated document in GATE XML format. The file saved by the client contains ANNIE's output in the default AnnotationSet and
the input document's HTML or XML mark-up in the "Original markups" AnnotationSet.
H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. 2002. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL-02).
ANNIE-RDF developed by the GATE group at the University of Sheffield (http;//www.gate.ac.uk; Cunningham et al., 2002) is an Information Extraction (IE) web service for English. It consists of the following main language processing tools: tokeniser, sentence splitter, POS tagger, coreference resolver and named entity recogniser.
The named entity recogniser identifies and categorizes entity names (such as persons, organizations, and location names), temporal expressions (dates and times), and certain types of numerical expressions (monetary values and percentages).
The text spans and annotations are exported into an RDF-XML ontology, in which the recognized named entities are instances according to the PROTON ontology (http://proton.semanticweb.org/).
H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. 2002. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL-02).
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
Grammar Error Correction Corpus for Czech (GECCC) consists of 83 058 sentences and covers four diverse domains, including essays written by native students, informal website texts, essays written by Romani ethnic minority children and teenagers and essays written by nonnative speakers. All domains are professionally annotated for GEC errors in a unified manner, and errors were automatically categorized with a Czech-specific version of ERRANT released at https://github.com/ufal/errant_czech
The dataset was introduced in the paper Czech Grammar Error Correction with a Large and Diverse Corpus that was accepted to TACL. Until published in TACL, see the arXiv version: https://arxiv.org/pdf/2201.05590.pdf
This version fixes double annotation errors in train and dev M2 files, and also contains more metadata information.
Fine-tuned Czech TinyLlama model (https://huggingface.co/BUT-FIT/CSTinyLlama-1.2B) and Czech GPT2 small model (https://huggingface.co/lchaloupsky/czech-gpt2-oscar) to generate lyrics of song sections based on the provided syllable counts, keywords and rhyme scheme. The TinyLlama-based model yields better results, however, the GPT2-based model can run locally.
Both models are discussed in a Bachelor Thesis: Generation of Czech Lyrics to Cover Songs.
The ultimate aim of the project is to compile a representative historical corpus of written German for the years 1650-1800. The complete GerManC corpus will contain 2000 word samples from nine genres