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An interpretability framework for similar case matching
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AI
DOI：10.1177/1088467X251395622.png)
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Abstract
En 中文
Similar Case Matching (SCM) plays a pivotal role in the legal system by facilitating the efficient identification of similar cases for legal professionals. While previous research has primarily concentrated on enhancing the performance of SCM models, the aspect of interpretability has been neglected. To bridge the gap, this study proposes an integrated pipeline framework for interpretable SCM. The framework comprises four modules: judicial feature sentence identification, case matching, feature sentence alignment, and conflict resolution. In contrast to current SCM methods, our framework first extracts feature sentences within a legal case that contain essential information. Then it conducts case matching based on these extracted features. Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity. In instances where the results of case matching and feature sentence alignment exhibit conflicts, the conflict resolution module resolves these inconsistencies. Experimental results demonstrate a significant improvement, with our framework achieving a final score of 76.91%, exceeding the baseline by 30%. The experimental results show the effectiveness of our proposed framework, establishing a new benchmark for interpretable SCM.
Keywords:
Similar case matching
interpretability
pipeline framework
legal text analysis
benchmarking in legal AI
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0.8
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72
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1.3K
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