|Title||Information Extraction in the Medical Domain|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Ghoulam, A, Barigou, F, Ghalem, B, Meziane, F|
|Journal||Journal of Information Technology Research (JITR)|
|Keywords||ctronic Medical Report, Extraction of Semantic Relations, Information Extraction, Medical Named Entities Recognition, Medical Relation Extraction|
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.