A Silver Standard Biomedical Corpus for Arabic Language

The rapidly growing data in many areas, as well as in the biomedical domain, require the assistance of information extraction systems to acquire the much needed knowledge about specific entities such as proteins, drugs, or diseases practically within a short time. Annotated corpora serve the purpose...

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Main Authors: Nada Boudjellal, Huaping Zhang, Asif Khan, Arshad Ahmad, Rashid Naseem, Lin Dai
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/8896659
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author Nada Boudjellal
Huaping Zhang
Asif Khan
Arshad Ahmad
Rashid Naseem
Lin Dai
author_facet Nada Boudjellal
Huaping Zhang
Asif Khan
Arshad Ahmad
Rashid Naseem
Lin Dai
author_sort Nada Boudjellal
collection DOAJ
description The rapidly growing data in many areas, as well as in the biomedical domain, require the assistance of information extraction systems to acquire the much needed knowledge about specific entities such as proteins, drugs, or diseases practically within a short time. Annotated corpora serve the purpose of facilitating the process of building NLP systems. While colossal work has been done in this area for English language, other languages like Arabic seem to lack these resources, especially in the healthcare area. Therefore, in this work, we present a method to develop a silver standard medical corpus for the Arabic language with a dictionary as a minimal supervision tool. The corpus contains 49,856 sentences tagged with 13 entity types corresponding to a subset of UMLS (Unified Medical Language System) concept types. The evaluation of a subset of corpus showed the efficiency of the method used to annotate it with 90% accuracy.
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institution DOAJ
issn 1076-2787
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language English
publishDate 2020-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-c99a1e6d85e84365a3d3ba4bb16862db2025-08-20T03:24:03ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88966598896659A Silver Standard Biomedical Corpus for Arabic LanguageNada Boudjellal0Huaping Zhang1Asif Khan2Arshad Ahmad3Rashid Naseem4Lin Dai5School of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaDepartment of Computer Science, City University of Science and Information Technology, Peshawar, PakistanDepartment of IT and Computer Science, Pak-Austria Fachhochschule: Institute of Applied Sciences & Technology, Haripur, PakistanSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaThe rapidly growing data in many areas, as well as in the biomedical domain, require the assistance of information extraction systems to acquire the much needed knowledge about specific entities such as proteins, drugs, or diseases practically within a short time. Annotated corpora serve the purpose of facilitating the process of building NLP systems. While colossal work has been done in this area for English language, other languages like Arabic seem to lack these resources, especially in the healthcare area. Therefore, in this work, we present a method to develop a silver standard medical corpus for the Arabic language with a dictionary as a minimal supervision tool. The corpus contains 49,856 sentences tagged with 13 entity types corresponding to a subset of UMLS (Unified Medical Language System) concept types. The evaluation of a subset of corpus showed the efficiency of the method used to annotate it with 90% accuracy.http://dx.doi.org/10.1155/2020/8896659
spellingShingle Nada Boudjellal
Huaping Zhang
Asif Khan
Arshad Ahmad
Rashid Naseem
Lin Dai
A Silver Standard Biomedical Corpus for Arabic Language
Complexity
title A Silver Standard Biomedical Corpus for Arabic Language
title_full A Silver Standard Biomedical Corpus for Arabic Language
title_fullStr A Silver Standard Biomedical Corpus for Arabic Language
title_full_unstemmed A Silver Standard Biomedical Corpus for Arabic Language
title_short A Silver Standard Biomedical Corpus for Arabic Language
title_sort silver standard biomedical corpus for arabic language
url http://dx.doi.org/10.1155/2020/8896659
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