Corpus domain effects on distributional semantic modeling of medical terms | Zendy

Serguei Pakhomov | Zendy; Greg P. Finley | Zendy; Reed McEwan | Zendy; Yan Wang | Zendy; Genevieve B. Melton | Zendy

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Corpus domain effects on distributional semantic modeling of medical terms

Author(s) -

Serguei Pakhomov,

Greg P. Finley,

Reed McEwan,

Yan Wang,

Genevieve B. Melton

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw529

Subject(s) - computer science , semantic similarity , benchmark (surveying) , semantics (computer science) , information retrieval , distributional semantics , natural language processing , similarity (geometry) , task (project management) , artificial intelligence , unified medical language system , domain (mathematical analysis) , biomedical text mining , text mining , mathematical analysis , mathematics , management , geodesy , economics , image (mathematics) , programming language , geography

Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available; however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.

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