Document Detail

Language-independent automatic acquisition of morphological knowledge from synonym pairs.
MedLine Citation:
PMID:  10566324     Owner:  NLM     Status:  MEDLINE    
Medical words exhibit a rich and productive morphology. Beyond simple inflection, derivation and composition are a common way to form new words. Morphological knowledge is therefore very important for any medical language processing application. Whereas rich morphological resources are available for the English medical language with the UMLS Specialist Lexicon, no such resources are publicly available for French or most other languages. We propose a simple and powerful method to help acquire automatically such knowledge. This method takes advantage of the synonym terms present in medical terminologies. In a bootstrapping step, it detects morphologically related words from which it learns "derivation rules". In an expansion step, it then applies these rules to the whole vocabulary available. Our goal is to acquire data for French and other languages for which they are not available. However, to evaluate the efficiency of the method, we tested it on English in a setting which is close to that prevailing for French, and we confronted its results to those obtained with the Specialist lexical variant generation tool.
N Grabar; P Zweigenbaum
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Proceedings / AMIA ... Annual Symposium. AMIA Symposium     Volume:  -     ISSN:  1531-605X     ISO Abbreviation:  Proc AMIA Symp     Publication Date:  1999  
Date Detail:
Created Date:  2000-02-01     Completed Date:  2000-02-01     Revised Date:  2009-11-18    
Medline Journal Info:
Nlm Unique ID:  100883449     Medline TA:  Proc AMIA Symp     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  77-81     Citation Subset:  IM    
DIAM-Service d'Informatique Médicale, DSI, Paris, France.
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MeSH Terms
Automatic Data Processing*
Subject Headings*
Terminology as Topic*

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