{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:15:22Z","timestamp":1791944122983,"version":"4.3.4"},"reference-count":19,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>This article outlines a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside\u2013outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14%. Due to the lack of research efforts in the literature dedicated to studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.<\/jats:p>","DOI":"10.3390\/data5030060","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T04:46:01Z","timestamp":1594701961000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4374-3675","authenticated-orcid":false,"given":"Nasser","family":"Alshammari","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1714-1948","authenticated-orcid":false,"given":"Saad","family":"Alanazi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"McLoughlin, L. (2009). Colloquial Arabic (Levantine), Routledge.","DOI":"10.4324\/9780203880746"},{"key":"ref_2","unstructured":"Alanazi, S. (2017). A Named Entity Recognition System Applied to Arabic Text in the Medical Domain. [Ph.D. Thesis, Staffordshire University]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shaalan, K., and Raza, H. (2008). Arabic named entity recognition from diverse text types. International Conference on Natural Language Processing, Springer.","DOI":"10.1007\/978-3-540-85287-2_42"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Konkol, M., and Konop\u00edk, M. (2015). Segment representations in named entity recognition. International Conference on Text, Speech, and Dialogue, Springer.","DOI":"10.1007\/978-3-319-24033-6_7"},{"key":"ref_5","unstructured":"Demiros, I., Boutsis, S., Giouli, V., Liakata, M., Papageorgiou, H., and Piperidis, S. (2019). Named Entity Recognition in Greek Texts. [Ph.D. Thesis, Aristotle University of Thessaloniki]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mozharova, V.A., and Loukachevitch, N.V. (2016). Combining knowledge and CRF-based approach to named entity recognition in Russian. International Conference on Analysis of Images, Social Networks and Texts, Springer.","DOI":"10.1007\/978-3-319-52920-2_18"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3383306","article-title":"Named Entity Recognition and Classification for Punjabi Shahmukhi","volume":"19","author":"Ahmad","year":"2020","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inform. Process. (TALLIP)"},{"key":"ref_8","unstructured":"Algahtani, S.M. (2012). Arabic Named Entity Recognition: A Corpus-Based Study. [Ph.D. Thesis, University of Manchester]."},{"key":"ref_9","first-page":"53","article-title":"A rule based persons names Arabic extraction system","volume":"11","author":"Elsebai","year":"2009","journal-title":"Commun. IBIMA"},{"key":"ref_10","unstructured":"Torisawa, K. (2007, January 28\u201330). Exploiting Wikipedia as external knowledge for named entity recognition. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic."},{"key":"ref_11","unstructured":"Alruily, M. (2012). Using Text Mining to Identify Crime Patterns from Arabic Crime News Report Corpus, De Montfort University."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1162\/COLI_a_00178","article-title":"A survey of arabic named entity recognition and classification","volume":"40","author":"Shaalan","year":"2014","journal-title":"Comput. Linguist."},{"key":"ref_13","unstructured":"(2020, April 30). King Abdullah Bin Abdulaziz Arabic Health Encyclopedia. Available online: https:\/\/kaahe.org\/."},{"key":"ref_14","unstructured":"Diab, M. (2009, January 22\u201323). 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Med."},{"key":"ref_18","first-page":"1094","article-title":"Madamira: A fast, comprehensive tool for morphological analysis and disambiguation of arabic","volume":"14","author":"Pasha","year":"2014","journal-title":"LREC"},{"key":"ref_19","unstructured":"Habash, N., Rambow, O., and Roth, R. (2009, January 22\u201323). MADA+ TOKAN: A toolkit for Arabic tokenization, diacritization, morphological disambiguation, POS tagging, stemming and lemmatization. Proceedings of the 2nd International Conference on Arabic Language Resources and Tools (MEDAR), Cairo, Egypt."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:58Z","timestamp":1760176258000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,13]]},"references-count":19,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["data5030060"],"URL":"https:\/\/doi.org\/10.3390\/data5030060","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,13]]}}}