A Retrieval-Augmented Contrastive Framework for Legal Case Retrieval Based on Event Information

Changyong Fan, Nankai Lin, Dong Zhou, Yongmei Zhou, Aimin Yang
Proceedings of the 16th Asian Conference on Machine Learning, PMLR 260:415-430, 2025.

Abstract

Similar case retrieval is a crucial aspect of the legal retrieval field, significantly contributing to LegalAI systems. This task aims to retrieve cases that are highly relevant to the query case, thereby enhancing the efficiency of legal practitioners. Recent methods have leveraged the rich semantic knowledge of pre-trained models, greatly improving retrieval performance. However, these methods often overlook key legal elements within the complex language structures of case texts, such as legal event information that can impact case outcomes and judgments. This oversight results in the underutilization of critical case information. To address this issue, we proposed RAEvent, a similar case retrieval contrastive framework augmented by legal event information. This framework utilizes an enhanced case event information database to provide auxiliary information for case retrieval and employs contrastive learning techniques to better extract similar features in cases. In comparison to a range of baseline approaches, the results of our experiments highlight the efficacy of our framework. Moreover, our research provides fresh perspectives and makes a valuable contribution to the ongoing studies in similar case retrieval tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v260-fan25b, title = {A Retrieval-Augmented Contrastive Framework for Legal Case Retrieval Based on Event Information}, author = {Fan, Changyong and Lin, Nankai and Zhou, Dong and Zhou, Yongmei and Yang, Aimin}, booktitle = {Proceedings of the 16th Asian Conference on Machine Learning}, pages = {415--430}, year = {2025}, editor = {Nguyen, Vu and Lin, Hsuan-Tien}, volume = {260}, series = {Proceedings of Machine Learning Research}, month = {05--08 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v260/main/assets/fan25b/fan25b.pdf}, url = {https://proceedings.mlr.press/v260/fan25b.html}, abstract = {Similar case retrieval is a crucial aspect of the legal retrieval field, significantly contributing to LegalAI systems. This task aims to retrieve cases that are highly relevant to the query case, thereby enhancing the efficiency of legal practitioners. Recent methods have leveraged the rich semantic knowledge of pre-trained models, greatly improving retrieval performance. However, these methods often overlook key legal elements within the complex language structures of case texts, such as legal event information that can impact case outcomes and judgments. This oversight results in the underutilization of critical case information. To address this issue, we proposed RAEvent, a similar case retrieval contrastive framework augmented by legal event information. This framework utilizes an enhanced case event information database to provide auxiliary information for case retrieval and employs contrastive learning techniques to better extract similar features in cases. In comparison to a range of baseline approaches, the results of our experiments highlight the efficacy of our framework. Moreover, our research provides fresh perspectives and makes a valuable contribution to the ongoing studies in similar case retrieval tasks.} }
Endnote
%0 Conference Paper %T A Retrieval-Augmented Contrastive Framework for Legal Case Retrieval Based on Event Information %A Changyong Fan %A Nankai Lin %A Dong Zhou %A Yongmei Zhou %A Aimin Yang %B Proceedings of the 16th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Vu Nguyen %E Hsuan-Tien Lin %F pmlr-v260-fan25b %I PMLR %P 415--430 %U https://proceedings.mlr.press/v260/fan25b.html %V 260 %X Similar case retrieval is a crucial aspect of the legal retrieval field, significantly contributing to LegalAI systems. This task aims to retrieve cases that are highly relevant to the query case, thereby enhancing the efficiency of legal practitioners. Recent methods have leveraged the rich semantic knowledge of pre-trained models, greatly improving retrieval performance. However, these methods often overlook key legal elements within the complex language structures of case texts, such as legal event information that can impact case outcomes and judgments. This oversight results in the underutilization of critical case information. To address this issue, we proposed RAEvent, a similar case retrieval contrastive framework augmented by legal event information. This framework utilizes an enhanced case event information database to provide auxiliary information for case retrieval and employs contrastive learning techniques to better extract similar features in cases. In comparison to a range of baseline approaches, the results of our experiments highlight the efficacy of our framework. Moreover, our research provides fresh perspectives and makes a valuable contribution to the ongoing studies in similar case retrieval tasks.
APA
Fan, C., Lin, N., Zhou, D., Zhou, Y. & Yang, A.. (2025). A Retrieval-Augmented Contrastive Framework for Legal Case Retrieval Based on Event Information. Proceedings of the 16th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 260:415-430 Available from https://proceedings.mlr.press/v260/fan25b.html.

Related Material