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22. Large Corpus of Czech Parliament Plenary Hearings
- Creator:
- Kratochvíl, Jonáš, Polák, Peter, and Bojar, Ondřej
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- audio and corpus
- Subject:
- ASR and Czech
- Language:
- Czech
- Description:
- 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.
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
23. LiFR-Law. Corpus of Paraphrased Czech Administrative Texts with Reading Comprehension for Readability Studies
- Creator:
- Cinková, Silvie, Chromý, Jan, Šamánková, Jana, Hořeňovská, Karolína, Kettnerová, Václava, Kolářová, Veronika, Kubištová, Hana, and Panevová, Jarmila
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- readability, legal texts, legal domain, reading comprehension, corpus, and survey
- Language:
- Czech
- Description:
- LiFR-Law is a corpus of Czech legal and administrative texts with measured reading comprehension and a subjective expert annotation of diverse textual properties based on the Hamburg Comprehensibility Concept (Langer, Schulz von Thun, Tausch, 1974). It has been built as a pilot data set to explore the Linguistic Factors of Readability (hence the LiFR acronym) in Czech administrative and legal texts, modeling their correlation with actually observed reading comprehension. The corpus is comprised of 18 documents in total; that is, six different texts from the legal/administration domain, each in three versions: the original and two paraphrases. Each such document triple shares one reading-comprehension test administered to at least thirty readers of random gender, educational background, and age. The data set also captures basic demographic information about each reader, their familiarity with the topic, and their subjective assessment of the stylistic properties of the given document, roughly corresponding to the key text properties identified by the Hamburg Comprehensibility Concept.
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
24. LiFR-Law. Corpus of Paraphrased Czech Administrative Texts with Reading Comprehension for Readability Studies (2023-10-08)
- Creator:
- Cinková, Silvie, Chromý, Jan, Šamánková, Jana, Hořeňovská, Karolína, Kettnerová, Václava, Kolářová, Veronika, Kubištová, Hana, and Panevová, Jarmila
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- readability, legal texts, legal domain, reading comprehension, corpus, and survey
- Language:
- Czech
- Description:
- LiFR-Law is a corpus of Czech legal and administrative texts with measured reading comprehension and a subjective expert annotation of diverse textual properties based on the Hamburg Comprehensibility Concept (Langer, Schulz von Thun, Tausch, 1974). It has been built as a pilot data set to explore the Linguistic Factors of Readability (hence the LiFR acronym) in Czech administrative and legal texts, modeling their correlation with actually observed reading comprehension. The corpus is comprised of 18 documents in total; that is, six different texts from the legal/administration domain, each in three versions: the original and two paraphrases. Each such document triple shares one reading-comprehension test administered to at least thirty readers of random gender, educational background, and age. The data set also captures basic demographic information about each reader, their familiarity with the topic, and their subjective assessment of the stylistic properties of the given document, roughly corresponding to the key text properties identified by the Hamburg Comprehensibility Concept. Changes to the previous version and helpful comments • File names of the comprehension test results (self-explanatory) • Corrected one erroneous automatic evaluation rule in the multiple-choice evaluation (zahradnici_3, TRUE and FALSE had been swapped) • Evaluation protocols for both question types added into Folder lifr_formr_study_design • Data has been cleaned: empty responses to multiple-choice questions were re-inserted. Now, all surveys are considered complete that have reader’s subjective text evaluation complete (these were placed at the very end of each survey). • Only complete surveys (all 7 content questions answered) are represented. We dropped the replies of six users who did not complete their surveys. • A few missing responses to open questions have been detected and re-inserted. • The demographic data contain all respondents who filled in the informed consent and the demographic details, with respondents who did not complete any test survey (but provided their demographic details) in a separate file. All other data have been cleaned to contain only responses by the regular respondents (at least one completed survey).
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
25. LiFR-Lite
- Creator:
- Cinková, Silvie, Chromý, Jan, Hořeňovská, Karolína, Kettnerová, Václava, Kolářová, Veronika, Panevová, Jarmila, and Ševčíková, Magda
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- education, readability, reading comprehension, and text corpora
- Language:
- Czech
- Description:
- Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
26. LiFR-Lite (2021-11-05)
- Creator:
- Cinková, Silvie, Chromý, Jan, Hořeňovská, Karolína, Kettnerová, Václava, Kolářová, Veronika, Kubištová, Hana, Panevová, Jarmila, and Ševčíková, Magda
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- education, readability, reading comprehension, and text corpora
- Language:
- Czech
- Description:
- Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
27. Manual Re-evaluation of Translation Quality of WMT 2018 English-Czech systems
- Creator:
- Popel, Martin, Tomková, Markéta, and Tomek, Jakub
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- machine translation, manual evaluation, fluency, adequacy, and Translation Turing test
- Language:
- Czech and English
- Description:
- This data set contains four types of manual annotation of translation quality, focusing on the comparison of human and machine translation quality (aka human-parity). The machine translation system used is English-Czech CUNI Transformer (CUBBITT). The annotations distinguish adequacy, fluency and overall quality. One of the types is Translation Turing test - detecting whether the annotators can distinguish human from machine translation. All the sentences are taken from the English-Czech test set newstest2018 (WMT2018 News translation shared task www.statmt.org/wmt18/translation-task.html), but only from the half with originally English sentences translated to Czech by a professional agency.
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
28. OAGK Keyword Generation Dataset
- Creator:
- Çano, Erion
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- keyword extraction and supervised keyword generation
- Language:
- English
- Description:
- OAGK is a keyword extraction/generation dataset consisting of 2.2 million abstracts, titles and keyword strings from cientific articles. Texts were lowercased and tokenized with Stanford CoreNLP tokenizer. No other preprocessing steps were applied in this release version. Dataset records (samples) are stored as JSON lines in each text file. This data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released under ODC-BY licence. This data (OAGK Keyword Generation Dataset) is released under CC-BY licence (https://creativecommons.org/licenses/by/4.0/). If using it, please cite the following paper: Çano, Erion and Bojar, Ondřej, 2019, Keyphrase Generation: A Text Summarization Struggle, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, June 2019, Minneapolis, USA
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
29. OAGKX Keyword Generation Dataset
- Creator:
- Çano, Erion
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- keyword extraction, supervised keyword generation, and abstractive keyphrasing
- Language:
- English
- Description:
- OAGKX is a keyword extraction/generation dataset consisting of 22674436 abstracts, titles and keyword strings from scientific articles. The texts were lowercased and tokenized with Stanford CoreNLP tokenizer. No other preprocessing steps were applied in this release version. Dataset records (samples) are stored as JSON lines in each text file. The data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released under ODC-BY license. This data (OAGKX Keyword Generation Dataset) is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using it, please cite the following paper: Çano Erion, Bojar Ondřej. Keyphrase Generation: A Multi-Aspect Survey. FRUCT 2019, Proceedings of the 25th Conference of the Open Innovations Association FRUCT, Helsinki, Finland, Nov. 2019 To reproduce the experiments in the above paper, you can use the first 100000 lines of part_0_0.txt file.
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
30. OAGL Paper Metadata Dataset
- Creator:
- Çano, Erion
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- Paper Length Prediction, Scientific Papers Corpus, and Scientific Publication Metadata
- Language:
- English
- Description:
- OAGL is a paper metadata dataset consisting of 17528680 records which comprise various scientific publication attributes like abstracts, titles, keywords, publication years, venues, etc. The last field of each record is the page length of the corresponding publication. Dataset records (samples) are stored as JSON lines in each text file. The data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released under ODC-BY license. This data (OAGL Paper Metadata Dataset) is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using it, please cite the following paper: Çano Erion, Bojar Ondřej: How Many Pages? Paper Length Prediction from the Metadata. NLPIR 2020, Proceedings of the the 4th International Conference on Natural Language Processing and Information Retrieval, Seoul, Korea, December 2020.
- Rights:
- Creative Commons - Attribution 4.0 International (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/, and PUB
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