煤矿开采
计算机科学
条件随机场
变压器
领域(数学)
领域知识
命名实体识别
独创性
人工智能
自然语言处理
煤
数据挖掘
工程类
系统工程
电气工程
数学
电压
法学
政治学
纯数学
任务(项目管理)
废物管理
创造力
作者
Na Xu,Yanxiang Liang,Chaoran Guo,Bo Meng,Xueqing Zhou,Yuting Hu,Bo Zhang
出处
期刊:Engineering, Construction and Architectural Management
[Emerald (MCB UP)]
日期:2023-12-27
标识
DOI:10.1108/ecam-05-2023-0512
摘要
Purpose Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts. Design/methodology/approach Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence. Findings Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods. Originality/value This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.
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