Judgment2vec: Apply Graph Analytics to Searching and Recommendation of Similar Judgments
This work addresses the problem of time-consuming case searches for legal professionals, though it is incremental as it applies existing graph and NLP methods to a new legal dataset.
The research tackled automating judgment text similarity analysis to reduce labor hours in legal searches by comparing expert-labeled similarity scores with Node2vec-based scores derived from a knowledge graph, achieving significant efficiency improvements.
In court practice, legal professionals rely on their training to provide opinions that resolve cases, one of the most crucial aspects being the ability to identify similar judgments from previous courts efficiently. However, finding a similar case is challenging and often depends on experience, legal domain knowledge, and extensive labor hours, making veteran lawyers or judges indispensable. This research aims to automate the analysis of judgment text similarity. We utilized a judgment dataset labeled as the "golden standard" by experts, which includes human-verified features that can be converted into an "expert similarity score." We then constructed a knowledge graph based on "case-article" relationships, ranking each case using natural language processing to derive a "Node2vec similarity score." By evaluating these two similarity scores, we identified their discrepancies and relationships. The results can significantly reduce the labor hours required for legal searches and recommendations, with potential applications extending to various fields of information retrieval.