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.
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.
OAGS is a title generation dataset consisting of 34993700 abstracts and titles from scientific 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. The data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released under ODC-BY licence. This data (OAGS Title 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, "Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study", INLG 2019, The 12th International Conference on Natural Language Generation, November 2019, Tokyo, Japan. To reproduce the experiments in the above paper, you can use oags_train1.txt, oags_train2.txt, oags_train3.txt, oags_test.txt and oags_val.txt files. If you need more data samples you can get them from oags_train_backup.txt and oags_val-test_backup.txt.
OAGSX is a title generation dataset consisting of 34408509 abstracts and titles 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 (OAGSX Title Generation Dataset) is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/).
If using it, please consider citing also the following paper:
Çano Erion, Bojar Ondřej. Two Huge Title and Keyword Generation Corpora of Research Articles.
LREC 2020, Proceedings of the the 12th International Conference on Language Resources and Evaluation,
Marseille, France, May 2020.
This corpus contains annotations of translation quality from English to Czech in seven categories on both segment- and document-level. There are 20 documents in total, each with 4 translations (evaluated by each annotator in paralel) of 8 segments (can be longer than one sentence). Apart from the evaluation, the annotators also proposed their own, improved versions of the translations.
There were 11 annotators in total, on expertise levels ranging from non-experts to professional translators.
ParaDi 2.0. is a dictionary of single verb paraphrases of Czech verbal multiword expressions - light verb constructions and idiomatic verb constructions. Moreover, it provides an elaborated set of morphological, syntactic and semantic features, including information on aspectual counterparts of verbs or paraphrasability conditions of given verbs.
The format of ParaDi has been designed with respect to both human and machine readability - the dictionary is represented as a plain table in TSV format, as it is a flexible and language-independent data format.
ParaDi 2.0. is a dictionary of single verb paraphrases of Czech verbal multiword expressions - light verb constructions and idiomatic verb constructions. Moreover, it provides an elaborated set of morphological, syntactic and semantic features, including information on aspectual counterparts of verbs or paraphrasability conditions of given verbs.
The format of ParaDi has been designed with respect to both human and machine readability - the dictionary is represented as a plain table in TSV format, as it is a flexible and language-independent data format.
This package contains polysemy graphs constructed on the basis of different sense chaining algorithms (representing different polysemy theories: prototype, exemplar and radial). The detailed description of all files is contained in the README.md file.
Experimental materials, data and R scripts used in the paper "Garden-path sentences and the diversity of their
(mis)representations" (Ceháková - Chromý, 2023).
Input data, individual experimental annotations, and a complete and detailed overview of the measured results related to the experiment described in the referenced paper.