UIE-Based Relational Extraction Task for Mine Hoist Fault Data

Abstract

Information extraction is pivotal in natural language processing, where the goal is to convert unstructured text into structured information. A significant challenge in this domain is the diversity and specific needs of various processing tasks. Traditional approaches typically utilize separate frameworks for different information extraction tasks, such as named entity recognition and relationship extraction, which hampers their uniformity and scalability. In this study, this study introduce a Universal Information Extraction (UIE) framework combined with a cue learning strategy, significantly improving the efficiency and accuracy of extracting mine hoist fault data. Initially, domain-specific data is manually labeled to fine-tune the model, and the accuracy is further enhanced by constructing negative examples during this fine-tuning process. The model then focuses on faults using the Structured Extraction Language (SEL) and a schema-based prompt syntax, the Structural Schema Instructor (SSI), which targets and extracts key information from the fault data to meet specific domain requirements. Experimental results show that UIE substantially improves the processing efficiency and the F1 accuracy of the extracted mine hoist fault data, with the fine-tuned F1 score increasing from 23.59% to 92.51%.