Abstract
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.- Anthology ID:
- 2021.findings-emnlp.257
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2997–3001
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.257
- DOI:
- 10.18653/v1/2021.findings-emnlp.257
- Cite (ACL):
- Yefei Teng and WenHan Chao. 2021. Argumentation-Driven Evidence Association in Criminal Cases. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2997–3001, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Argumentation-Driven Evidence Association in Criminal Cases (Teng & Chao, Findings 2021)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2021.findings-emnlp.257.pdf