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

IF 4.1 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Hang He, Chao Ma, Shan Ye, Wenqiang Tang, Yuxuan Zhou, Zhen Yu, Jiaxin Yi, Li Hou, Mingcai Hou
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引用次数: 0

Abstract

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 mainstream geoscience databases for geological information mining, making it more challenging for some researchers 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 identify 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 entities 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 algorithms, demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

基于提示学习的低资源中文地质文本命名实体识别
地质报告包含丰富的数据和文字信息,是地质学家从事地质调查和科学研究的重要成果。随着科学技术的飞速发展,地质领域积累了大量的文字报告。然而,在主流的地质科学数据库中,许多非热点话题和非英语地区的地质信息挖掘被忽视,这使得一些研究人员从这些文本中提取必要的信息变得更具挑战性。自然语言处理(NLP)在处理大量文本数据方面具有明显的优势。本文旨在利用 NLP 技术从中文地质文本中识别地质命名实体。我们提出了 Ro-BERTa-Prompt-Tuning-NER方法,该方法利用了提示学习的概念,只需要少量的注释数据,就能在低资源数据集配置中训练出识别地质命名实体的优秀模型。RoBERTa 层通过动态词向量捕捉基于上下文的信息和长距离依赖关系。最后,我们在构建的地质命名实体识别(GNER)数据集上进行了实验。实验结果表明,在四种基线算法中,所提模型的F1得分最高,达到80.64%,证明了将该模型用于地质文本命名实体识别的可靠性和鲁棒性。
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来源期刊
Journal of Earth Science
Journal of Earth Science 地学-地球科学综合
CiteScore
5.50
自引率
12.10%
发文量
128
审稿时长
4.5 months
期刊介绍: Journal of Earth Science (previously known as Journal of China University of Geosciences), issued bimonthly through China University of Geosciences, covers all branches of geology and related technology in the exploration and utilization of earth resources. Founded in 1990 as the Journal of China University of Geosciences, this publication is expanding its breadth of coverage to an international scope. Coverage includes such topics as geology, petrology, mineralogy, ore deposit geology, tectonics, paleontology, stratigraphy, sedimentology, geochemistry, geophysics and environmental sciences. Articles published in recent issues include Tectonics in the Northwestern West Philippine Basin; Creep Damage Characteristics of Soft Rock under Disturbance Loads; Simplicial Indicator Kriging; Tephra Discovered in High Resolution Peat Sediment and Its Indication to Climatic Event. The journal offers discussion of new theories, methods and discoveries; reports on recent achievements in the geosciences; and timely reviews of selected subjects.
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