Comparing methods to extract technical content for technological intelligence

We are developing indicators for the emergence of science and technology (S&T) topics. To do so, we extract information from various S&T information resources. This paper compares alternative ways of consolidating messy sets of key terms [e.g., using Natural Language Processing on abstracts...

Full description

Saved in:
Bibliographic Details
Published in:Journal of engineering and technology management 2014-04, Vol.32, p.97-109
Main Authors: Newman, Nils C., Porter, Alan L., Newman, David, Trumbach, Cherie Courseault, Bolan, Stephanie D.
Format: Article
Language:English
Subjects:
Commonality
Consolidation
Dye-sensitized solar cells
Emergence
Fuzzy
Fuzzy logic
Fuzzy set theory
Information technology
Intelligence
Natural language processing
Principal components analysis
Studies
Tech mining
Technological emergence
Term clustering
Topic modeling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We are developing indicators for the emergence of science and technology (S&T) topics. To do so, we extract information from various S&T 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.
ISSN:0923-4748
1879-1719
DOI:10.1016/j.jengtecman.2013.09.001