A Named Entity Recognition System Applied to Arabic Text in the Medical Domain
At the sixth Message Understanding Conference (MUC-6) in 1995, Named Entity Recognition (NER) was recognised as an essential sub field of information extraction and as an important contribution to natural language processing. The goal of NER is to extract specific predefined list of entities, which can include proper names, numerical expression and temporal expression. This paper introduces NAMERAMA which is a novel NER system based on Bayesian Belief Network (BBN). It extracts disease names, symptoms, treatment methods, and diagnosis methods from modern Arabic text in the medical domain. The results of the developed system shows that BBN performance is promising with 71.05% overall F-measure. The highest F-measure score was achieved in recognising disease names with 98.10% while the lowest was in recognising symptoms with 41.66%.
Keywords: Named Entity Recognition, Bayesian Belief Network, Natural language processing, Machine learning.
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ABOUT THE AUTHORS
Saad Alanazi
Saad Alanazi received his B.S degree in Computer Science from Aljouf University in 2007 and M.S. degree in Computer Science from Ball State University in 2011. He is currently a Ph.D student at Staffordshire University. His research interests include natural language processing and text mining.
Bernadette Sharp
Bernadette Sharp is Professor of Applied AI at Staffordshire University. She is a Chartered IT Professional Fellow of the British Computer Society. She has published over 100 referred publications in the areas of applied artificial intelligence, natural language processing and knowledge discovery. She is Chair and editor of the International Workshop for Natural Language Processing and Cognitive Science (NLPCS) and the International Conference on Agents and Artificial Intelligence between 2009-2010. She has BSc in Computer Mathematics, MPhil in Statistical Forecasting, PhD in Natural Language Processing.
Clare Stanier
Clare Stanier is a Senior Lecturer in Information Systems at Staffordshire University. She is a Senior Fellow of the Higher Education Academy, a member of the British Computer Society and a programme committee member for TLAD, the HEA sponsored international workshop on the Teaching, Learning and Assessment of Databases. Her research interests are in data management and in Big Data strategies and technologies. She has an MSc in Business Intelligence and a PhD in Computer Science.
Saad Alanazi
Saad Alanazi received his B.S degree in Computer Science from Aljouf University in 2007 and M.S. degree in Computer Science from Ball State University in 2011. He is currently a Ph.D student at Staffordshire University. His research interests include natural language processing and text mining.
Bernadette Sharp
Bernadette Sharp is Professor of Applied AI at Staffordshire University. She is a Chartered IT Professional Fellow of the British Computer Society. She has published over 100 referred publications in the areas of applied artificial intelligence, natural language processing and knowledge discovery. She is Chair and editor of the International Workshop for Natural Language Processing and Cognitive Science (NLPCS) and the International Conference on Agents and Artificial Intelligence between 2009-2010. She has BSc in Computer Mathematics, MPhil in Statistical Forecasting, PhD in Natural Language Processing.
Clare Stanier
Clare Stanier is a Senior Lecturer in Information Systems at Staffordshire University. She is a Senior Fellow of the Higher Education Academy, a member of the British Computer Society and a programme committee member for TLAD, the HEA sponsored international workshop on the Teaching, Learning and Assessment of Databases. Her research interests are in data management and in Big Data strategies and technologies. She has an MSc in Business Intelligence and a PhD in Computer Science.
