Text mining for biomedicine -- Lexical granularity for automatic indexing and means to achieve it : the case of Swedish MeSH® -- Expanding terms with medical ontologies to improve a multi-label text categorization system -- Using biomedical terminological resources for information retrieval -- Automatic alignment of medical terminologies with general dictionaries for an efficient information retrieval -- Translation of biomedical terms by inferring rewriting rules -- Lexical enrichment of biomedical ontologies -- Word sense disambiguation in biomedical applications : a machine learning approach -- Information extraction of protein phosphorylation from biomedical literature -- CorTag : a language for a contextual tagging of the words within their sentence -- Analyzing the text of clinical literature for question answering -- Discourse processing for text mining -- Neural network approach implementing non-linear relevance feedback to improve the performance of medical information retrieval systems -- Extracting patient case profiles with domain-specific semantic categories -- Identification of sequence variants of genes from biomedical literature : the OSIRIS approach -- Verification of uncurated protein annotations -- Software tool for biomedical information extraction (and beyond) -- Problems-solving map extraction with collective intelligence analysis and language engineering -- Seekbio : retrieval of spatial relations for system biology -- Analysing clinical notes for translation research : back to the future
Summary
"This book provides relevant theoretical frameworks and the latest empirical research findings in biomedicine information retrieval as it pertains to linguistic granularity"--Provided by publisher
Notes
"Premier reference source"--Cover
Bibliography
Includes bibliographical references (pages 378-413) and index
