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A deep learning methodology for automatic extraction and discovery of technical intelligence

Author

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  • Xu, Jianguo
  • Guo, Lixiang
  • Jiang, Jiang
  • Ge, Bingfeng
  • Li, Mengjun

Abstract

It is imperative and arduous to acquire product and business intelligence of global technical market. In this paper, a deep learning methodology is proposed to automatically extract and discover vital technical information from large-scale news dataset. More specifically, six kinds of technical elements are first defined to provide the concrete syntax information. Next, the CRF-BiLSTM approach is used to automatically extract technical entities, in which a conditional random field (CRF) layer is added on top of bidirectional long short-term memory (BiLSTM) layer. Then, three indicators including timeliness, influence and innovativeness are designed to evaluate the value of intelligence comprehensively. Finally, as a case study, technical news on three military-related websites is utilized to illustrate the efficiency and effectiveness of the foregoing methodology with the result of 80.82 (F-score) in comparison to four other models. In more detail, data on unmanned systems are extracted to summarize the state-of-the-art, and track up-to-the-minute innovations and developments in this field.

Suggested Citation

  • Xu, Jianguo & Guo, Lixiang & Jiang, Jiang & Ge, Bingfeng & Li, Mengjun, 2019. "A deep learning methodology for automatic extraction and discovery of technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 339-351.
    • Handle: RePEc:eee:tefoso:v:146:y:2019:i:c:p:339-351
    DOI: 10.1016/j.techfore.2019.06.004
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    1. Zhai, Dongsheng & Zhai, Liang & Li, Mengyang & He, Xijun & Xu, Shuo & Wang, Feifei, 2022. "Patent representation learning with a novel design of patent ontology: Case study on PEM patents," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    3. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    4. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

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