This machine translation test set contains 2223 Czech sentences collected within the FAUST project (https://ufal.mff.cuni.cz/grants/faust, http://hdl.handle.net/11234/1-3308).
Each original (noisy) sentence was normalized (clean1 and clean2) and translated to English independently by two translators.
The Feature-based (exponential model) Tagger is a fast implementation of the Czech tagger developed at UFAL and described in the PDT 1.0 documentation (Czech Language Tagging page). In order to get the best possible results, the tagger requires preprocessing by a Czech morphological module with a very high coverage. This module covers a superset of the Czech "FM" morphology. Both the morphological module and the tagger are supplied as binary executables, together with all necessary precompiled Czech data. Input must be in the ISO Latin 2 (iso-8859-2) code and follow the csts.dtd definition, and output is produced in the same way (ISO Latin 2 code, csts.dtd). (As is the case with many of the tools provided with PDT 1.0, both executables also accept - and then produce - a "simplified SGML", which is not a real, valid SGML, but simply contains at least the tags for words, punctuation, and sentence breaks, one item per line.)
ForFun is a database of linguistic forms and their syntactic functions built with the use of the multi-layer annotated corpora of Czech, the Prague Dependency Treebanks. The purpose of the Prague Database of Forms and Functions (ForFun) is to help the linguists to study the form-function relation, which we assume to be one of the principal tasks of both theoretical linguistics and natural language processing.
A prototypical question to be asked is "What purposes does a preposition 'po' serve for" or "What are the linguistic means in the sentence that can express the meaning 'a destination of an action'?". There are almost 1500 distinct forms (besides the 'po' preposition) and 65 distinct functions (besides the 'destination').
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 GrandStaff-LMX dataset is based on the GrandStaff dataset described in the "End-to-end optical music recognition for pianoform sheet music" paper by Antonio Ríos-Vila et al., 2023, https://doi.org/10.1007/s10032-023-00432-z .
The GrandStaff-LMX dataset contains MusicXML and Linearized MusicXML encodings of all systems from the original datase, suitable for evaluation with the TEDn metric. It also contains the GrandStaff official train/dev/split.
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.