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Information Extraction in the Medical Domain

Information Extraction in the Medical Domain

Aicha Ghoulam, Fatiha Barigou, Ghalem Belalem
Copyright: © 2017 |Pages: 18
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch075
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MLA

Ghoulam, Aicha, et al. "Information Extraction in the Medical Domain." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1840-1857. https://doi.org/10.4018/978-1-5225-1759-7.ch075

APA

Ghoulam, A., Barigou, F., & Belalem, G. (2017). Information Extraction in the Medical Domain. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1840-1857). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch075

Chicago

Ghoulam, Aicha, Fatiha Barigou, and Ghalem Belalem. "Information Extraction in the Medical Domain." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1840-1857. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch075

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Abstract

Information Extraction (IE) is a natural language processing (NLP) task whose aim is to analyse texts written in natural language to extract structured and useful information such as named entities and semantic relations between them. Information extraction is an important task in a diverse set of applications like bio-medical literature mining, customer care, community websites, personal information management and so on. In this paper, the authors focus only on information extraction from clinical reports. The two most fundamental tasks in information extraction are discussed; namely, named entity recognition task and relation extraction task. The authors give details about the most used rule/pattern-based and machine learning techniques for each task. They also make comparisons between these techniques and summarize the advantages and disadvantages of each one.

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