As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate unders...As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.展开更多
矿产资源地质报告中蕴含大量专家经验及基础地质知识。快速准确地从海量矿产资源文本中抽取形成结构化知识已成为目前研究热点,命名实体识别是信息抽取与知识挖掘的重要步骤。针对矿产资源地质文本中存在实体长度长、专业术语多、实体...矿产资源地质报告中蕴含大量专家经验及基础地质知识。快速准确地从海量矿产资源文本中抽取形成结构化知识已成为目前研究热点,命名实体识别是信息抽取与知识挖掘的重要步骤。针对矿产资源地质文本中存在实体长度长、专业术语多、实体嵌套等问题,已有基于深度学习的命名实体识别直接应用在矿产资源领域性能低下,本文提出了一种矿产资源命名实体识别深度学习模型:ALBERT(A Lite Bidirectional Encoder Representations from Transformers)-BiLSTM(Bi-directional Long Short-Term Memory)-CRF(Conditional Random Field),通过ALBERT预训练语言模型获取地质文本丰富语义特征,同时结合汉字拼音、字形和词边界特征来共同作为嵌入层,从而提高对复杂实体的识别能力。本文方法在人民日报、电子简历数据集及构建的矿产资源数据集上进行实验,结果表明提出方法在准确率、召回率、F1值上分别达到70.97%、64.33%、67.49%。展开更多
基金financially supported by the Natural Science Foundation of China(Grant No.42301492)the National Key R&D Program of China(Grant Nos.2022YFF0711600,2022YFF0801201,2022YFF0801200)+3 种基金the Major Special Project of Xinjiang(Grant No.2022A03009-3)the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(Grant No.KF-2022-07014)the Opening Fund of the Key Laboratory of the Geological Survey and Evaluation of the Ministry of Education(Grant No.GLAB 2023ZR01)the Fundamental Research Funds for the Central Universities。
文摘As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge.While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition accuracy.This study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological entities.The model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer algorithm.The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.
文摘矿产资源地质报告中蕴含大量专家经验及基础地质知识。快速准确地从海量矿产资源文本中抽取形成结构化知识已成为目前研究热点,命名实体识别是信息抽取与知识挖掘的重要步骤。针对矿产资源地质文本中存在实体长度长、专业术语多、实体嵌套等问题,已有基于深度学习的命名实体识别直接应用在矿产资源领域性能低下,本文提出了一种矿产资源命名实体识别深度学习模型:ALBERT(A Lite Bidirectional Encoder Representations from Transformers)-BiLSTM(Bi-directional Long Short-Term Memory)-CRF(Conditional Random Field),通过ALBERT预训练语言模型获取地质文本丰富语义特征,同时结合汉字拼音、字形和词边界特征来共同作为嵌入层,从而提高对复杂实体的识别能力。本文方法在人民日报、电子简历数据集及构建的矿产资源数据集上进行实验,结果表明提出方法在准确率、召回率、F1值上分别达到70.97%、64.33%、67.49%。
