Representing biomedical knowledge in the UMLS semantic network
High performance medical libraries
Development and evaluation of a computerized admission diagnosis encoding system
Computers and Biomedical Research
Computers and Biomedical Research
Extracting taxonomic relationships from on-line definitional sources using LEXING
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
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Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
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Generative content models for structural analysis of medical abstracts
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Computer Methods and Programs in Biomedicine
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HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
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Interpretation of semantic propositions in free-text documents such as MEDLINE citations would provide valuable support for biomedical applications, and several approaches to semantic interpretation are being pursued in the biomedical informatics community. In this paper, we describe a methodology for interpreting linguistic structures that encode hypernymic propositions, in which a more specific concept is in a taxonomic relationship with a more general concept. In order to effectively process these constructions, we exploit underspecified syntactic analysis and structured domain knowledge from the Unified Medical Language System (UMLS). After introducing the syntactic processing on which our system depends, we focus on the UMLS knowledge that supports interpretation of hypernymic propositions. We first use semantic groups from the Semantic Network to ensure that the two concepts involved are compatible; hierarchical information in the Metathesaurus then determines which concept is more general and which more specific. A preliminary evaluation of a sample based on the semantic group Chemicals and Drugs provides 83% precision. An error analysis was conducted and potential solutions to the problems encountered are presented. The research discussed here serves as a paradigm for investigating the interaction between domain knowledge and linguistic structure in natural language processing, and could also make a contribution to research on automatic processing of discourse structure. Additional implications of the system we present include its integration in advanced semantic interpretation processors for biomedical text and its use for information extraction in specific domains. The approach has the potential to support a range of applications, including information retrieval and ontology engineering.