Automatic Population of Italian Medical Thesauri: A Morphosemantic Approach
Automatic Population of Italian Medical Thesauri: A Morphosemantic Approach
In the age of Semantic Web, one of the most valuable challenges is the one connected with the information extraction from raw data. Information must be managed with sophisticated linguistic and computational architectures, which are able to approach the semantic dimension of words and sentences. In this paper we propose a morphosemantic method for the automatic creation and population of medical lexical resources. Our approach is grounded on a list of neoclassical formative elements pertaining to the medical domain an on a large sized corpus of medical diagnoses. The outcomes of this work are automatically built electronic dictionaries and thesauri and an annotated corpus for the NLP in the medical domain.
Thesauri, Information analysis, Electronic dictionaries, Automatic populations, Lexical resources, Distributed computer systems, Large-sized, Automatic creations, INFORMATION RETRIEVAL, SEMANTIC WEB, Diagnosis, Information Retrieval, Information retrieval, Computational architecture, Data mining; Distributed computer systems; Information analysis; Information retrieval; Semantic Web; Thesauri, Automatic creations; Automatic populations; Computational architecture; Electronic dictionaries; Large-sized; Lexical resources; Medical domains, Diagnosis; Information Extraction; Information Retrieval, Medical domains, Data mining, Information Extraction, Semantic Web
Thesauri, Information analysis, Electronic dictionaries, Automatic populations, Lexical resources, Distributed computer systems, Large-sized, Automatic creations, INFORMATION RETRIEVAL, SEMANTIC WEB, Diagnosis, Information Retrieval, Information retrieval, Computational architecture, Data mining; Distributed computer systems; Information analysis; Information retrieval; Semantic Web; Thesauri, Automatic creations; Automatic populations; Computational architecture; Electronic dictionaries; Large-sized; Lexical resources; Medical domains, Diagnosis; Information Extraction; Information Retrieval, Medical domains, Data mining, Information Extraction, Semantic Web
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