Semantic Networks in Artificial Intelligence
Semantic Networks in Artificial Intelligence
Introduction to the special issue on evaluating word sense disambiguation systems
Natural Language Engineering
A hybrid approach for searching in the semantic web
Proceedings of the 13th international conference on World Wide Web
Neural Networks Letter: Cogent confabulation
Neural Networks
Towards understanding of natural language: neurocognitive inspirations
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
The evaluation of semantic tools to support physicians in the extraction of diagnosis codes
USAB'07 Proceedings of the 3rd Human-computer interaction and usability engineering of the Austrian computer society conference on HCI and usability for medicine and health care
Expert Systems with Applications: An International Journal
Concept map construction from text documents using affinity propagation
Journal of Information Science
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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.