Document Detail


Dialect topic modeling for improved consumer medical search.
MedLine Citation:
PMID:  21346955     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Access to health information by consumers is hampered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, common and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations using consumer questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.
Authors:
Steven P Crain; Shuang-Hong Yang; Hongyuan Zha; Yu Jiao
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Publication Detail:
Type:  Journal Article     Date:  2010-11-13
Journal Detail:
Title:  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium     Volume:  2010     ISSN:  1942-597X     ISO Abbreviation:  AMIA Annu Symp Proc     Publication Date:  2010  
Date Detail:
Created Date:  2011-02-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101209213     Medline TA:  AMIA Annu Symp Proc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  132-6     Citation Subset:  IM    
Affiliation:
Georgia Institute of Technology, Atlanta, GA.
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