基于双步抽取的低资源中文工业领域术语抽取方法  

A Dual-Step Extraction Method for Chinese Industrial Terminology in Low-Resource Environments

作　　者：邢季 刘瑾[1] 张建伟 XING Ji;LIU Jin;ZHANG Jianwei(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气学院,上海201620

出　　处：《武汉大学学报（理学版）》2024年第3期329-340,共12页Journal of Wuhan University:Natural Science Edition

基　　金：科技部科技创新2030“新一代人工智能”重大项目(2020AAA0109300)。

摘　　要：工业领域数据集由各类操作文档、维修文档、设备图纸,以及不断增加的工单和工作记录等数据组成。现有的通用术语抽取方法在中文情景下效果受限,同时先验资源的匮乏也导致了传统的监督学习流程难以实现,因此业内常见模型在工业垂直领域术语抽取任务中的效果并不理想。为了解决上述问题,提出了一种基于预抽取和细化微调的双步抽取策略。在XLNet预训练模型的基础上,结合字符、字形和字音特征,增强了模型捕获语义信息的能力。采用LSTM编码器-解码器模型,生成含有错别字的负样本扩充数据集,旨在提升模型对噪音文本的鲁棒性。将本文方法应用于汽车工业领域,实验结果显示,本方法在该垂直领域的性能比现有传统方法提高了17%,充分证明了其有效性。The industrial dataset comprises various types of operation documents,maintenance manuals,and equipment drawings,as well as an increasing number of work orders and job records.Existing general-term extraction methods are limited in effectiveness in Chinese contexts,and the scarcity of prior resources also makes traditional supervised learning processes challenging to implement.Therefore,standard models in the industry do not perform ideally in the task of extracting vertical domain terminology in industrial fields.This study introduces a dual-step extraction strategy based on pre-extraction and fine-tuning refinement to address these issues.the approach enhances the models ability to capture semantic information using the XLNet pre-trained model and incorporating character,glyph,and phonetic features.Moreover,the paper employs an LSTM encoder-decoder model to generate negative samples containing typographical errors to expand the dataset,aiming to improve the models robustness to noisy text.Applying the method in this article to the field of the automotive industry,experimental results show that our method improves performance in the industrial vertical domain by 17%over the existing traditional methods,demonstrating its effectiveness.

关 键 词：深度学习 自然语言处理 术语抽取 低资源抽取 XLNet 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]