计算机技术与发展2025，Vol.35Issue(5)：197-204,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0390
基于K-BERT的测井文本分类方法研究
Research on Logging Text Classification Method Based on K-BERT
摘要
Abstract
In the field of petroleum exploration and development,the processing and classification of well logging text data are crucial steps for enhancing the efficiency and accuracy of well logging data interpretation.However,well logging texts contain a plethora of pro-fessional terminology and complex data structures,which limit the effectiveness of traditional text classification techniques when dealing with domain-specific data,thus failing to meet practical application requirements.To address this issue,we propose an improved K-BERT text classification method.This method integrates the text feature extraction capabilities of the K-BERT model and TextCNN.By incorporating a knowledge graph specific to the well logging domain,K-BERT embeds domain knowledge into the model,enhancing its understanding of professional terminology and complex semantics,and improving the model's semantic capture performance in domain-specific text classification.On the other hand,TextCNN leverages the characteristics of convolutional neural networks to effectively extract local features of texts and capture detailed textual information,further enhancing classification accuracy and robustness.The com-bination of these two techniques provides an innovative solution for the classification of well logging texts.Experimental comparisons demonstrate that the proposed method outperforms traditional text classification models in terms of macro precision,macro recall,and macro F1 score,validating its effectiveness and superiority in domain-specific text classification.关键词
K-BERT/TextCNN/测井文本/文本分类/测井知识图谱Key words
K-BERT/TextCNN/logging text/text classification/logging knowledge graph分类
信息技术与安全科学引用本文复制引用
曹茂俊,肖阳..基于K-BERT的测井文本分类方法研究[J].计算机技术与发展,2025,35(5):197-204,8.基金项目
中石油科技术开发项目(2021DJ4001) （2021DJ4001）
黑龙江省建设项目(YJSKCSZ_202309) （YJSKCSZ_202309）
黑龙江省高等教育教学改革项目(SJGY20220253) （SJGY20220253）
