skip to main content
10.1145/3578741.3578775acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmlnlpConference Proceedingsconference-collections
research-article

Extraction of key information from Chinese text of gas station safety accident cases

Published:06 March 2023Publication History

ABSTRACT

Keyword matching-based text structuring of gas station safety accident cases can automatically perform keyword extraction and store them in the database, and then the structured data can be subjected to multi-dimensional statistics, visual analysis, knowledge graph management and law mining. 516 cases of gas station safety accidents since 1981, which belong to unstructured electronic text data, were collected in four ways. Through the establishment of a keyword dictionary, 516 case texts were extracted and stored in a structured way using a progressive model of exact matching, phrase matching and intelligent matching+manual verification. The actual evaluation results show that the success rate of exact matching is 30% - 87%, the cumulative success rate of phrase matching is 70% - 100%, the cumulative matching rate of intelligent matching+manual verification is 91% - 100%, the overall matching effect is good, and the method is practical and useful.

References

  1. Yu Jie, Ji Bin, Liu Lei, Joint extraction method for Chinese medical events [J]. Computer Science, 2021,48 (11): 287-293Google ScholarGoogle Scholar
  2. Guo Weijie, Bao Xiao'an. Key Information Extraction Model of Unstructured Text in Knowledge Database [J]. Computer Simulation, 2021,38 (09): 357-360+394Google ScholarGoogle Scholar
  3. Wang Xiaoya.Text analysis of lung diseases based on BERT semantic embedding and its application [D]. Chengdu: University of Electronic Science and Technology of China, 2021. DOI: 10.27005/d.cnki.gdzku.2021.002173Google ScholarGoogle ScholarCross RefCross Ref
  4. Cao Wenkang.Research and Implementation of Fact based Case Big Data Query Technology [D]. Nanjing: Nanjing Normal University, 2021. DOI: 10.27,245/d.cnki.gnjsu.2021.001,053Google ScholarGoogle Scholar
  5. Zhu Chaoqun. Research on Chinese text summarization method based on improved TextRank [D]. Wuhan: Wuhan Research Institute of Posts and Telecommunications, 2021. DOI: 10.27386/d.cnki.gwyky.2021.000044Google ScholarGoogle ScholarCross RefCross Ref
  6. Li Bohan, Li Honglian. Research on Text Abstract Based on Multi task Learning [J]. Computer Knowledge and Technology, 2020,16 (31): 20-25+48. DOI: 10.14,004/j.cnki.ckt.2020.3561Google ScholarGoogle Scholar
  7. Li Yuman.Research on key information extraction for forestry texts [D]. Beijing: Beijing Forestry University, 2020. DOI: 10.26949/d.cnki.gblyu.2020.001040Google ScholarGoogle ScholarCross RefCross Ref
  8. Yu Simiao.A Study on the Classification of Collection Document Titles for Subject Word Matching [D]. Shenyang: Liaoning University of Engineering and Technology, 2020. DOI: 10.27210/d.cnki.glnju.2020.000256Google ScholarGoogle ScholarCross RefCross Ref
  9. Fan Xi.Research on Web System for Extracting Key Information from Patent Web Pages [D]. Beijing: Beijing University of Posts and Telecommunications, 2020. DOI: 10.26969/d.cnki.gbydu.2020.002054Google ScholarGoogle ScholarCross RefCross Ref
  10. Chen Zhibo, Li Yuman, Xu Fu, Feng Guoming, Shi Dongyu, Cui Xiaohui. Research on forestry text key information extraction based on TextRank and cluster filtering [J]. Journal of Agricultural Machinery, 2020,51 (05): 207-214+172Google ScholarGoogle Scholar
  11. Huang Pu.Research on key technologies of text information extraction optimization and system implementation [D]. Beijing: Beijing University of Posts and Telecommunications, 2019Google ScholarGoogle Scholar
  12. Li Chenliang.Text summarization model based on neural network joint learning [D]. Beijing: Beijing University of Posts and Telecommunications, 2019Google ScholarGoogle Scholar
  13. Liu Wen, Wang Jin, Li Rui, Design and implementation of key information extraction system for court judgment [J]. Journal of Hubei University of Technology, 2018, 33 (01): 63-67Google ScholarGoogle Scholar

Index Terms

  1. Extraction of key information from Chinese text of gas station safety accident cases
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          MLNLP '22: Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing
          December 2022
          406 pages
          ISBN:9781450399067
          DOI:10.1145/3578741

          Copyright © 2022 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 March 2023

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed limited
        • Article Metrics

          • Downloads (Last 12 months)19
          • Downloads (Last 6 weeks)3

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format