Judicial Text Relation Extraction Based on Prompt Tuning

With the acceleration of the digital transformation in the judicial field, the need for automated processing and analysis of voluminous legal documents becomes increasingly imperative. Relationship extraction, a pivotal step in comprehending these documents, is crucial for automated case analysis, discovery of legal facts, and prediction of judgments. However, the idiosyncrasies of judicial texts, such as their intricate linguistic structures, specialized terminology, and implicit semantic relationships, present significant challenges to the efficacy of traditional text analysis methods. Traditional relation extraction techniques often fail to adapt well to professional terms, intricate linguistic structures and implicit semantics in judicial texts. Moreover, the general lack of training data tailored to the specific context of the legal domain hampers the ability of models to learn effectively and accurately extract relationships from judicial texts. This paper introduces a novel approach utilizing pre-trained models for prompt-based fine-tuning to achieve relationship extraction in judicial texts. Leveraging the robust language comprehension capabilities of large-scale pre-trained models, we have designed domain-specific prompt templates to guide the model in more effectively capturing and understanding key information within legal texts. This method not only enhances the accuracy of relationship extraction but also significantly reduces reliance on extensive annotated data. The effectiveness and superiority of our approach are corroborated by test results on a judicial text dataset.