计算机与数字工程2024，Vol.52Issue(6)：1815-1820,1876,7.DOI:10.3969/j.issn.1672-9722.2024.06.037
基于BERT-BiLSTM-CRF模型的地质领域实体识别研究
Named Entity Recognition in Geological Field Based on BERT-BiLSTM-CRF
庄子浩 1焦守龙 1孙琛皓1
作者信息
- 1. 中国石油大学(华东)计算机科学与技术学院 青岛 266580
- 折叠
摘要
Abstract
Recognition of geological entities based on geological texts plays a major role in mining and analyzing data for geo-logical researchers.This technology is also the basis for building knowledge graph in the geological field and lots of upper-level ap-plications.At present,the research of entity recognition in the field of geology is still under development,with fewer applications,but the amount of geological data is increasing exponentially,so data processing technology is particularly important.Therefore this paper proposes a named entity recognition technology based on the BERT-BiLSTM-CRF model and constraint rules to assist geologi-cal professionals in processing geological data.First,the BERT layer receives the input text sequences,and converts them into word vectors with contextual features.Next the word vectors are input into the BiLSTM layer to learn the contextual features,and the BiL-STM layer outputs the scores of every single chinese character.After this,the CRF layer integrates the scores from the BiLSTM layer and the implicit rules which are learned by itself,then the final comprehensive scores are output so as to select the best label.The experimental results show that compared with the traditional method and the popular deep learning method,the precision,recall,and F1 value of this method are all the highest values,which are 92.05%,94.82%,and 93.41%,respectively.关键词
命名实体识别/知识图谱/深度学习/地质领域/BERTKey words
named entity recognition/knowledge graph/deep learning/geological field/BERT分类
信息技术与安全科学引用本文复制引用
庄子浩,焦守龙,孙琛皓..基于BERT-BiLSTM-CRF模型的地质领域实体识别研究[J].计算机与数字工程,2024,52(6):1815-1820,1876,7.
