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Volume 34 Issue 5
Oct 2023
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Article Contents
Journal of Earth Science  > 2023  >  34(5): 1406-1417.
Qinjun Qiu, Miao Tian, Zhong Xie, Yongjian Tan, Kai Ma, Qingfang Wang, Shengyong Pan, Liufeng Tao. Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach. Journal of Earth Science, 2023, 34(5): 1406-1417. doi: 10.1007/s12583-022-1789-8
Citation: Qinjun Qiu, Miao Tian, Zhong Xie, Yongjian Tan, Kai Ma, Qingfang Wang, Shengyong Pan, Liufeng Tao. Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach. Journal of Earth Science, 2023, 34(5): 1406-1417. doi: 10.1007/s12583-022-1789-8
shu

Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

doi: 10.1007/s12583-022-1789-8
  • 1.

    State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Chinese Academy of Surveying and Mapping, Beijing 100036, China

  • 2.

    Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, MNR, Shanghai 20063, China

  • 3.

    Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China

  • 4.

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

  • 5.

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

  • 6.

    Chengdu Geological Environment Monitoring Station, Chengdu 610036, China

  • 7.

    Wuhan Zondy Cyber Science & Technology Co., Ltd., Wuhan 430074, China

More Information
  • Corresponding author: Kai Ma, makai@ctgu.edu.cn
  • Received Date: 03 Mar 2022
  • Accepted Date: 14 Nov 2022
    • Available Online: 14 Oct 2023
  • Issue Publish Date: 30 Oct 2023
    Abstract

  • Artificial intelligence (AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers (ALBERT)- Bi-directional Long Short-Term Memory (BiLSTM)-Conditional Random Fields (CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

     

    • Conflict of Interest
    The authors declare that they have no conflict of interest.
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