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Context-Aware Adaptive Hybrid Semantic Relatedness in Biomedical Science

Abstract Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1–6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to ... (more)
Created Date 2016
Contributor Emadzadeh, Ehsan (Author) / Gonzalez, Graciela (Advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Subject Computer science / Health sciences / Nanotechnology / biomedical informatics / natural language processing / semantic analysis / semantic relatedness / text mining
Type Doctoral Dissertation
Extent 94 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Biomedical Informatics 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis