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PubMed · 14728239

A data-driven approach for extracting "the most specific term" for ontology development.

Guergana K Savova·Marcelline Harris·Thomas Johnson·Serguei V Pakhomov·Christopher G Chute
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Abstract

We present a data-driven approach to extract the "most specific" terms relevant to an ontology of functioning, disability and health. The algorithm is a combination of statistical and linguistic approaches. The statistical filter is based on the frequency of the content words in a given text string; the linguistic heuristic is an extension of existing algorithms but goes beyond noun phrases and is formulated as a "complete syntactic node". Thus, it can be applied to any syntactic node of interest in the particular domain. Two test sets were marked by three experts. Test set 1 is a well-constructed text from pain abstracts; test set 2 is actual medical reports. Results are reported as recall, precision, F-score and rate of valid terms in false positives. A limitation of the current research is the relatively small test set.

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AlgorithmsHealthLinguisticsNatural Language ProcessingStatistics as TopicVocabulary, ControlledJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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Guergana K Savova, Marcelline Harris, Thomas Johnson, Serguei V Pakhomov, Christopher G Chute. 2003. A data-driven approach for extracting "the most specific term" for ontology development.. https://pubmed.ncbi.nlm.nih.gov/14728239/

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