摘要
针对传统方法在石油钻井领域由于检索词不标准、语义模糊导致检索结果偏差较大的问题,提出一种基于BERT(Bidirectional Encoder Representation from Transformers)孪生网络模型的注意力池化方法以提高文献相似度评估的准确率。首先使用爬虫技术采集石油钻井文献并清洗整理,然后利用5类石油钻井文献数据集评估指标对样本进行打分标注,最后结合钻井文献数据集特征,提出基于孪生BERT网络的注意力池化方法,对多特征样本进行整体语义表达。实验结果表明,相较于常规的池化方法,该模型能提升石油钻井文献相似度度量的效果,并具有一定的泛化性能。
In order to solve the problem that the retrieval results are biased due to the nonstandard keywords and fuzzy semantics in the petroleum drilling literature, an attention pooling method based on the Siamese BERT(Bidirectional Encoder Representation from Transformers) networks model is proposed to improve the accuracy of literature similarity evaluation. Firstly, crawler technology is used to collect and clean the petroleum drilling literature. Then, five evaluation indexes of the petroleum drilling literature data set are used to mark the samples. Finally, combined with the data characteristics of the drilling literature data set, the attention pooling method based on Siamese BERT networks is used to express the overall semantics of multi-feature samples. The experimental results show that compared with the conventional pooling method, this method can improve the effect of similarity measurement of petroleum drilling literature, and has a certain generalization performance.
作者
张岩
王斌
杨庆川
李玮
ZHANG Yan;WANG Bin;YANG Qingchuan;LI Wei(School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China;College of Petroleum Engineering,Northeast Petroleum University,Daqing 163318,China;Data Management Center,Anda Qingxin Oilfield Development Company Limited,Anda 151413,China)
出处
《吉林大学学报（信息科学版）》
CAS
2022年第2期188-197,共10页
Journal of Jilin University（Information Science Edition）
基金
国家自然科学基金资助项目(61873058)
黑龙江省自然科学基金重点资助项目(ZD2019F001)。
关键词
文献相似度
BERT网络
石油钻井文献
注意力池化
literature similarity
bidirectional encoder representation from transformers(BERT)network
petroleum drilling literature
attention pooling
