PICMET
Portland International Conference on Management of Engineering
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12R0456
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"Comparing Methods to Extract Technical Content for Technological Intelligence"
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Nils Newman * , IISC, United States
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Alan Porter, Georgia Institute of Technology, United States
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David Newman, University of California at Irvine, United States
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Cherie Courseault, University of New Orleans, United States
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Stephanie Bolan, Georgia Institute of Technology, United States
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* = Corresponding author
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We are developing indicators for the emergence of science and technology (S&T) topics. We are targeting various S&T information resources, including metadata (i.e., bibliographic information) and full text. We explore alternative text analysis approaches, principal components analysis (PCA) and topic modeling, to extract technical topic information. We analyze the topical content to pursue potential applications and innovation pathways. In this presentation we compare alternative ways of consolidating messy sets of key terms (e.g., using natural language processing (NLP) on abstracts and titles, together with various keyword sets). Our process includes combinations of stopword removal, fuzzy term matching, association rules, and tf-idf weighting. We compare PCA results to topic modeling results. Our key test set consists of 4104 Web of Science records on dye-sensitized solar cells (DSSCs). Results suggest good potential to enhance our technical intelligence payoffs from database searches on topics of interest.
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