A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry

Yuan Zhou, Heng Lin, Yufei Liu () and Wei Ding
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Yuan Zhou: Tsinghua University
Heng Lin: Huazhong University of Science and Technology
Yufei Liu: Tsinghua University
Wei Ding: Huazhong University of Science and Technology

Scientometrics, 2019, vol. 120, issue 1, No 9, 167-185

Abstract: Abstract There have been recent attempts to identify emerging technologies by using topic-based analysis, but many of them have methodological deficiencies. First, analyses are unsupervised, and unsupervised methods cannot incorporate supervised knowledge that is needed to better identify technological domains. Second, those methods lack semantic interpretation, as many of them still remain at word-level analyses, we developed a novel technology-identification method that uses a semi-supervised topic clustering model (Labeled Dirichlet Multi Mixture model) to integrate technological domain knowledge. The model also generates a sentence-level semantic technological topic description through the topic description method (Various-aspects Sentence-level Description) on information extraction. We used this novel method to analyze the technology of the 3D printing industry, and successfully identified emerging technologies by differentiating new topics from the traditional topics, the results effectively demonstrated the semantic technological topic description by showing sentences. This method could be of great interest to technology forecasters and relevant policy-makers.

Keywords: Emerging technologies; Semi-supervised; Topic model; Sentence-level; Technological description; 3D printing (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s11192-019-03126-8

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