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

• Published in: Technological forecasting & social change Vol. 146; pp. 339 - 351
• Main Authors: Xu, Jianguo, Guo, Lixiang, Jiang, Jiang, Ge, Bingfeng, Li, Mengjun
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.09.2019; Elsevier Science Ltd
• Subjects: Armed forces; Case studies; Conditional random fields; CRF-BiLSTM; Deep learning; Efficiency; Information management; Intelligence (information); Intelligence monitoring; Machine learning; Memory; Methodology; News; Organizational effectiveness; Technical information; Technical intelligence; Websites; Deep learning; Technical intelligence; CRF-BiLSTM; Intelligence monitoring
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2019.06.004

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. •A deep learning based methodology was proposed to automatically extract technical intelligence.•Technology can be described with a set of related elements such as requirement, product, and organization.•Three indicators including timeliness, influence and innovativeness were designed to measure high-value intelligence.•Results indicated that companies could be aware of global technical information with business news.