首页|Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

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Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual information. With the rapid development of science and technology, a large number of textual reports have accumulated in the field of geology. However, many non-hot topics and non-English speaking regions are neglected in main-stream geoscience databases for geological information mining, making it more challenging for some re-searchers to extract necessary information from these texts. Natural Language Processing (NLP) has obvious advantages in processing large amounts of textual data. The objective of this paper is to identi-fy geological named entities from Chinese geological texts using NLP techniques. We propose the Ro-BERTa-Prompt-Tuning-NER method, which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named enti-ties in low-resource dataset configurations. The RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word vectors. Finally, we conducted experiments on the constructed Geological Named Entity Recognition (GNER) dataset. Our experimental results show that the proposed model achieves the highest F1 score of 80.64％ among the four baseline algo-rithms, demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

Prompt LearningNamed Entity Recognition (NER)low resource geological texttext information miningbig datageology

Hang He、Chao Ma、Shan Ye、Wenqiang Tang、Yuxuan Zhou、Zhen Yu、Jiaxin Yi、Li Hou、Mingcai Hou

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State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Institute of Sedimentary Geology, College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China

Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu University of Technology, Chengdu 610059, China

School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key R&D Program of ChinaSichuan Provincial Youth Science&Technology Innovative Research Group FundDeeptime Digital Earth(DDE)Big Science ProgramIGCP 739

424882014217213742050104420501022023YFF08040002022JDTD0004

2024

10.1007/s12583-023-1944-8

地球科学学刊(英文版)
中国地质大学

地球科学学刊(英文版)

CSTPCD
影响因子：0.724
ISSN：1674-487X
年,卷(期)：2024.35(3)
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