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

查看全文 作　　者：Qinjun [1,2,3]Qiu;Miao [4,5]Tian;Zhong [2,3]Xie;Yongjian [4,5]Tan;Kai [4,5]Ma;Qingfang [6]Wang;Shengyong [7]Pan;Liufeng [2,3]Tao

机构地区：[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

出　　处：《Journal of Earth Science》索引2023年第34卷第5期,共12页

基　　金：the IUGS Deep-time Digital Earth (DDE) Big Science Program;financially supported by the National Key R&D Program of China (No.2022YFF0711601);the Natural Science Foundation of Hubei Province of China (No.2022CFB640);the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (No.GLAB 2023ZR01);the Fundamental Research Funds for the Central Universities,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 (No.2022-03-08);the Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities,MNR (NO.KFKT-2022-02);the Project of Chengdu Municipal Bureau of Planning and Natural Resources (No.5101012018002703)。 摘　　要：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. 关键词：ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

相关文献

参考文献（58）
引证文献（1）
耦合文献（0）

参考文献(58)

引证文献(1)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

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

参考文献(58)

引证文献(1)

用户提示