A Dual-Step Extraction Method for Chinese Industrial Terminology in Low-Resource Environments
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 model's 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 model's 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.
deep learningNLPterminology extractionlow-resource extractionXLNet
NETL
NSTL
万方数据
