Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
280 decisions were annotated by one trained annotator and then manually adjudicated by one trained curator. 70 decisions were annotated by two trained annotators and then manually adjudicated by one trained curator. Adjudication was conducted destructively, therefore dataset contains only the correct annotations and does not contain all original annotations.
Corpus was developed as training and testing material for text segmentation tasks. Dataset contains decision segmented into Header, Procedural History, Submission/Rejoinder, Court Argumentation, Footer, Footnotes, and Dissenting Opinion. Segmentation allows to treat different parts of text differently even if it contains similar linguistic or other features.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
COSTRA 1.0 is a dataset of Czech complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing.
The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation.
The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting “skeleton” in the sentence embedding space.
Costra 1.1 is a new dataset for testing geometric properties of sentence embeddings spaces. In particular, it concentrates on examining how well sentence embeddings capture complex phenomena such paraphrases, tense or generalization. The dataset is a direct expansion of Costra 1.0, which was extended with more sentences and sentence comparisons.
This is a document-aligned parallel corpus of English and Czech abstracts of scientific papers published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague, as reported in the institute's system Biblio. For each publication, the authors are obliged to provide both the original abstract in Czech or English, and its translation into English or Czech, respectively. No filtering was performed, except for removing entries missing the Czech or English abstract, and replacing newline and tabulator characters by spaces.
This is a parallel corpus of Czech and mostly English abstracts of scientific papers and presentations published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague. For each publication record, the authors are obliged to provide both the original abstract (in Czech or English), and its translation (English or Czech) in the internal Biblio system. The data was filtered for duplicates and missing entries, ensuring that every record is bilingual. Additionally, records of published papers which are indexed by SemanticScholar contain the respective link. The dataset was created from September 2022 image of the Biblio database and is stored in JSONL format, with each line corresponding to one record.
The database contains annotated reflective sentences, which fall into the categories of reflective writing according to Ullmann's (2019) model. The dataset is ready to replicate these categories' prediction using machine learning. Available from: https://anonymous.4open.science/repository/c856595c-dfc2-48d7-aa3d-0ccc2648c4dc/data
Eyetracked Multi-Modal Translation (EMMT) is a simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenarios. It contains monocular eye movement recordings, audio data and 4-electrode wearable electroencephalogram (EEG) data of 43 participants while engaged in sight translation supported by an image.
The details about the experiment and the dataset can be found in the README file.
We present a large corpus of Czech parliament plenary sessions. The corpus
consists of approximately 444 hours of speech data and corresponding text
transcriptions. The whole corpus has been segmented to short audio snippets
making it suitable for both training and evaluation of automatic speech
recognition (ASR) systems. The source language of the corpus is Czech, which
makes it a valuable resource for future research as only a few public datasets
are available for the Czech language.