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Comparing methods to extract technical content for technological intelligence
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DOI：10.1016/j.jengtecman.2013.09.001.png)
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摘要
En 中文
We are developing indicators for the emergence of science and technology (S&T) topics. To do so, we extract information from various SU information resources. This paper compares alternative ways of consolidating messy sets of key terms [e.g., using Natural Language Processing on abstracts and titles, together with various keyword sets]. Our process includes combinations of stopword removal, fuzzy term matching, association rules, and term commonality weighting. We compare topic modeling to Principal Components Analysis for a test set of 4104 abstract records on Dye-Sensitized Solar Cells. Results suggest potential to enhance understanding regarding technological topics to help track technological emergence. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Tech mining
Topic modeling
Term clustering
Technological emergence
Dye-sensitized solar cells
期刊
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3.9
论文数:
814
被引数:
1.7K
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