From Linguistic Resources to Medical Entity Recognition: a Supervised Morpho-syntactic Approach

Due to the importance of the information it conveys, Medical Entity Recognition is one of the most investigated tasks in Natural Language Processing. Many researches have been aiming at solving the issue of Text Extraction, also in order to develop Decision Support Systems in the field of Health Care. In this paper, we propose a Lexicon-grammar method for the automatic extraction from raw texts of the semantic information referring to medical entities and, furthermore, for the identification of the semantic categories that describe the located entities. Our work is grounded on an electronic dictionary of neoclassical formative elements of the medical domain, an electronic dictionary of nouns indicating drugs, body parts and internal body parts and a grammar network composed of morphological and syntactical rules in the form of Finite-State Automata. The outcome of our research is an Extensible Markup Language (XML) annotated corpus of medical reports with information pertaining to the medical Diseases, Treatments, Tests, Symptoms and Medical Branches, which can be reused by any kind of machine learning tool inthe medical domain.