Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211100119-6.doi: 10.11896/jsjkx.211100119

• Artificial Intelligence • Previous Articles     Next Articles

Analysis of Technology Trends Based on Deep Learning and Text Measurement

WEI Ru-ming1, CHEN Ruo-yu1, LI Han1, LIU Xu-hong1,2   

  1. 1 Laboratory of Data Science and Information Studies,Beijing Information Science and Technology University,Beijing 100101,China
    2 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing Information Science and Technology University,Beijing 100101,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:WEI Ru-ming,born in 1997,postgraduate.His main research interests include natural language processing and know-ledge graph.
    CHEN Ruo-yu,born in 1982,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include natural language processing,data mining,semantic network and so on.
  • Supported by:
    Qin Xin Talents Cultivation Program,Beijing Information Science & Technology University(2021),Promoting the Development of University Classification-key Research and Cultivation Projects－research on the Construction of an Ontology Deep Belief Network Model Suitable for Smart City Application Scenarios(2121YJPY225),Innovation Capacity Building of Scientific Research Institutions-Institute of Data Science and Information Analysis,Promote the Development of the Connotation of Colleges and Universities－an Innovative Scientific Research Platform Construction Project for Edge Computing(2020KYNH105).

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Abstract: Traditionally,technical trend analysis tasks need to be done by experienced analysts,involving a lot of literature review and data analysis work,which is time-consuming and labor-intensive.Facing the above problems,this paper proposes a technology trend analysis model based on deep learning and text measurement,and a domain specific named entity recognition(NER) algorithm based on the BERT_BiLSTM_CRF model is designed with optimized masking mechanism.Taking news and literatures texts in the field of integrated circuit as data set,a comparative study between BiLSTM_CRF,BERT_BiGRU_CRF and the optimized BERT_BiLSTM_CRF* model proposed in this paper is carried out.The performance of NER is compared and analyzed.Compared with other algorithms,the proposed algorithm reaches 88.6%(measured by F1 value),laying the foundation for technical trend analysis.Based on the characteristics of knowledge graphs that relationships can be naturally expressed,an innovative method that combines knowledge graphs with text measurement technology is proposed,and the results of technical trend analysis are visualized from various perspectives,and ultimately assist analysts to carry out intelligent analysis of technical trends.

Key words: Named entity recognition, Knowledge graph, BERT_BiLSTM_CRF, Text measurement, Technology trend analysis

CLC Number: 

  • TP391
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