Efficient Relation Extraction of Automobile Faults Based on Hidden Knowledge in Prompt Tuning Templates
Author:
Affiliation:
1. Shanghai University of Engineering Science,Shanghai,China,201620
2. Tongji University,Shanghai,China,201804
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10616250/10617814/10618078.pdf?arnumber=10618078
Reference8 articles.
1. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
2. An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain
3. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
4. Template-free Prompt Tuning for Few-shot NER
5. K-nearest neighbor
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