Incorporating Retrieval Information into the Truncation of Ranking Lists for Better Legal Search

Author:

Ma Yixiao1,Ai Qingyao2,Wu Yueyue1,Shao Yunqiu1,Liu Yiqun1,Zhang Min1,Ma Shaoping1

Affiliation:

1. Tsinghua University, Beijing, China

2. University of Utah, Salt Lake City, UT, USA

Funder

Natural Science Foundation of China

Tsinghua University Guoqiang Research Institute

National Key Research and Development Program of China

Publisher

ACM

Reference38 articles.

1. Where to stop reading a ranked list?

2. Arian Askari and Suzan Verberne. 2021. Combining lexical and neural retrieval with longformer-based summarization for effective case law retrieval. (2021). Arian Askari and Suzan Verberne. 2021. Combining lexical and neural retrieval with longformer-based summarization for effective case law retrieval. (2021).

3. Choppy: Cut Transformer for Ranked List Truncation

4. A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law

5. Daniel Cohen , Bhaskar Mitra , Oleg Lesota , Navid Rekabsaz , and Carsten Eickhoff . 2021. Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. arXiv preprint arXiv:2105.04651 ( 2021 ). Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, and Carsten Eickhoff. 2021. Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. arXiv preprint arXiv:2105.04651 (2021).

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4. CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs;Lecture Notes in Computer Science;2024

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