Argumentation-Driven Evidence Association in Criminal Cases
Yefei Teng, Wenhan Chao · 2021
Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction.Observably, evidence divided into the same subset usually supports the same claim.Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper.Experimental results on a real-world dataset demonstrate the effectiveness of our method.