Nonambiguous Concept Mapping in Medical Domain

Pawel Matykiewicz1,2, Wlodzislaw Duch1,3 and John Pestian2
1Department of Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.
2Department of Biomedical Informatics, Children's Hospital Research Foundation, Cincinnati, Ohio, USA, and
3School of Computer Engineering, Nanyang Technological University, Singapore.


Automatic annotation of medical texts for various natural language processing tasks is a very important goal that is still far from being accomplished. Semantic annotation of a free text is one of the necessary steps in this process. Disambiguation is frequently attempted using either rule-based or statistical approaches to semantical analysis. A neurocognitive approach for a nonambiguous concept mapping is proposed here. Concepts are taken from the Unified Medical Language System (UMLS) collection of ontologies. An active part of the whole semantic memory based on these concepts forms a graph of consistent concepts (GCC). The text is analyzed by spreading activation in the network that consist of GCC and related concepts in the semantic network. A scoring function is used for choosing the meaning of the concepts that fit in the best way to the current interpretation of the text. ULMS knowledge sources are not sufficient to fully characterize concepts and their relations. Annotated texts are used to learn new relations useful for disambiguation of word meanings.

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Reference: Matykiewicz P, Duch W, Pestian J, Nonambiguous Concept Mapping in Medical Domain, Lecture Notes in Artificial Intelligence, Vol. 4029 (2006) 941-950

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