.png){kind=icon}
Return
Comparing methods to extract technical content for technological intelligence
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jengtecman.2013.09.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Tech mining
Topic modeling
Term clustering
Technological emergence
Dye-sensitized solar cells
Journal
IF:
3.9
Papers:
814
Citations:
1.7K
Organization
Cited Papers
ShareSaveShareSaveShareSaveDatabase of NIH grants using machine-learned categories and graphical clustering.png)
NATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAIShareSaveShareSaveAutomated extraction and visualization of information for technological intelligence and forecasting
PREAI
ShareSaveEmpirically informing a technology delivery system model for an emerging technology: illustrated for dye-sensitized solar cellsmanagement
managementIF5.7
PREAI
ShareSaveBibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive mapsSCIENTOMETRICS
OAAI
SCIENTOMETRICSIF3.5
OAAIShareSave
View more