A Silver Standard Biomedical Corpus for Arabic Language

Nada Boudjellal; Huaping Zhang; Asif Khan; Arshad Ahmad; Rashid Naseem; Lin Dai

doi:10.1155/2020/8896659

A Silver Standard Biomedical Corpus for Arabic Language

Nada Boudjellal, Huaping Zhang^*, Asif Khan, Arshad Ahmad, Rashid Naseem, Lin Dai

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Article number	8896659
Journal	Complexity
Volume	2020
DOIs	https://doi.org/10.1155/2020/8896659
Publication status	Published - 2020

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title = "A Silver Standard Biomedical Corpus for Arabic Language",

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T1 - A Silver Standard Biomedical Corpus for Arabic Language

AU - Boudjellal, Nada

AU - Zhang, Huaping

AU - Khan, Asif

AU - Ahmad, Arshad

AU - Naseem, Rashid

AU - Dai, Lin

PY - 2020

Y1 - 2020

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AB - 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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A Silver Standard Biomedical Corpus for Arabic Language

Abstract

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