Efficient Relation Extraction of Automobile Faults Based on Hidden Knowledge in Prompt Tuning Templates

In this paper, we propose a relation extraction method based on the Prompt Tuning (PT) technique, which aims to improve the accuracy and efficiency of knowledge extraction in automotive fault texts. The method can be effectively applied to long-tail or less resourceful situations by mining the hidde...

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Bibliographic Details
Published inPhotonics & Electromagnetics Research Symposium (Online) pp. 1 - 6
Main Authors Zhang, Kai, Yan, Shujia, Chen, Qiang, Gao, Yingjie, Tong, Mei Song
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.04.2024
Subjects
Accuracy
Automobiles
Data mining
Data models
Predictive models
Task analysis
Training data
Online AccessGet full text
ISSN2831-5804
DOI10.1109/PIERS62282.2024.10618078

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Summary:In this paper, we propose a relation extraction method based on the Prompt Tuning (PT) technique, which aims to improve the accuracy and efficiency of knowledge extraction in automotive fault texts. The method can be effectively applied to long-tail or less resourceful situations by mining the hidden knowledge in templates. Experimental results demonstrate that the method performs better in dealing with long-tailed data with a minimal small of relation labels, successfully overcoming issues such as catastrophic forgetting and misrecognition. In a data ratio of approximately 15:1, the predicted F1 values for relation labels (representing performance failures) are improved by 4.15%, 1.24%, 1.26%, and 0.03%, respectively, compared to the best results obtained from the comparison model. Finally, the extracted triples (subject, relation, object) are imported into the Neo4j graph database to realize intelligent retrieval of automotive faults.
ISSN:2831-5804
DOI:10.1109/PIERS62282.2024.10618078