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

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

Technological Forecasting and Social Change, 2019, vol. 146, issue C, 339-351

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.

Keywords: Technical intelligence; CRF-BiLSTM; Deep learning; Intelligence monitoring (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:146:y:2019:i:c:p:339-351

DOI: 10.1016/j.techfore.2019.06.004

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