AlbMoRe is a sentiment analysis corpus of movie reviews in Albanian, consisting of 800 records in CSV format. Each record includes a text review retrieved from IMDb and translated in Albanian by the author. It also contains a 0 negative) or 1 (positive) label added by the author. The corpus is fully balanced, consisting of 400 positive and 400 negative reviews about 67 movies of different genres. AlbMoRe corpus is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using the data, please cite the following paper: Çano Erion. AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian. CoRR, abs/2306.08526, 2023. URL https://arxiv.org/abs/2306.08526.
AlbNER is a Named Entity Recognition corpus of Wikipedia sentences in Albanian, consisting of 900 records. The sentence tokens are manually labeled complying with the CoNLL-2003 shared task annotation scheme explained at https://aclanthology.org/W03-0419.pdf that uses I-ORG, B-ORG, I-PER, B-PER, I-LOC, B-LOC, I-MISC, B-MISC and O tags. AlbNER data are released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using AlbMoRe corpus, please cite the following paper: Çano Erion. AlbNER: A Corpus for Named Entity Recognition in Albanian. CoRR, abs/2309.08741, 2023. URL https://arxiv.org/abs/2309.08741.
AlbNews is a topic modeling corpus of news headlines in Albanian, consisting of 600 labeled samples and 2600 unlabeled samples. Each labeled sample includes a headline text retrieved from Albanian online news portals. It also contains one of the four labels: 'pol' for politics, 'cul' for culture, 'eco' for economy, and 'spo' for sport. Each of the unlabeled samples contain a headline text only.AlbTopic corpus is released under CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). If using the data, please cite the following paper:
Çano Erion, Lamaj Dario. AlbNews: A Corpus of Headlines for Topic Modeling in Albanian. CoRR, abs/2402.04028, 2024. URL: https://arxiv.org/abs/2402.04028.
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
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.