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.
Annotated list of dependency bigrams occurring in the PDT more than five times and having part-of-speech patterns that can possibly form a collocation. Each bigram is assigned to one of the six MWE categories by three annotators.
The dataset of handwritten Czech text lines, sourced from two chronicles (municipal chronicles 1931-1944, school chronicles 1913-1933).
The dataset comprises 25k lines machine-extracted from scanned pages, and provides manual annotation of text contents for a subset of size 2k.
HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular recently. We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes.
The HMM-based Tagger is a software for morphological disambiguation (tagging) of Czech texts. The algorithm is statistical, based on the Hidden Markov Models.
Environmental impact assessment (EIA) is the formal process used to predict the environmental consequences of a plan. We present a rule-based extraction system to mine Czech EIA documents. The extraction rules work with a set of documents enriched with morphological information and manually created vocabularies of terms supposed to be extracted from the documents, e.g. basic information about the project (address, ID company, ...), data on the impacts and outcomes (waste substances, endangered species, ...), a final opinion. The documents Notice of Intent contains the section BI2 with the information on the scope (capacity) of the plan.
KER is a keyword extractor that was designed for scanned texts in Czech and English. It is based on the standard tf-idf algorithm with the idf tables trained on texts from Wikipedia. To deal with the data sparsity, texts are preprocessed by Morphodita: morphological dictionary and tagger.
This package contains data sets for development and testing of machine translation of medical search short queries between Czech, English, French, and German. The queries come from general public and medical experts. and This work was supported by the EU FP7 project Khresmoi (European Comission contract No. 257528). The language resources are distributed by the LINDAT/Clarin project of the Ministry of Education, Youth and Sports of the Czech Republic (project no. LM2010013).
We thank Health on the Net Foundation for granting the license for the English general public queries, TRIP database for granting the license for the English medical expert queries, and three anonymous translators and three medical experts for translating amd revising the data.