title: A data-driven approach for extracting "the most specific term" for ontology development.
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.
authors.affiliation: Division of Medical Informatics Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Guergana K
authors.initials: GK
authors.lastname: Savova
authors.forename: Marcelline
authors.initials: M
authors.lastname: Harris
authors.forename: Thomas
authors.initials: T
authors.lastname: Johnson
authors.forename: Serguei V
authors.initials: SV
authors.lastname: Pakhomov
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
mesh_terms: D000465:Algorithms; D006262:Health; D008037:Linguistics; D009323:Natural Language Processing; D013223:Statistics as Topic; D018875:Vocabulary, Controlled