The Competition on Legal Information Extraction/Entailment (COLIEE) is held
annually to encourage advancements in the automatic processing of legal texts.
Processing legal documents is challenging due to the intricate structure and
meaning of legal language. In this paper, we outline our strategies for
tackling Task 2, Task 3, and Task 4 in the COLIEE 2023 competition. Our
approach involved utilizing appropriate state-of-the-art deep learning methods,
designing methods based on domain characteristics observation, and applying
meticulous engineering practices and methodologies to the competition. As a
result, our performance in these tasks has been outstanding, with first places
in Task 2 and Task 3, and promising results in Task 4. Our source code is
available at this https URL

该论文介绍了在 COLIEE 2023 竞赛中处理 Task 2、Task 3 和 Task 4 的策略，使用了先进的深度学习方法、基于领域特征观察的设计方法，以及严谨的工程实践和方法论，在这些任务中取得了优秀的成绩，并在 Task 2 和 Task 3 中获得了第一名，Task 4 中也有良好结果。源代码可在指定的 URL 获得。

COLIEE 2023 的 CAPTAIN：法律信息检索和蕴含任务的高效方法

CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information  Retrieval and Entailment Tasks

This paper describes our submission to the Competition on Legal Information
Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for
tasks 1 and 2. Task 1 is a legal case retrieval task, which involves reading a
new case and extracting supporting cases from the provided case law corpus to
support the decision. Task 2 is the legal case entailment task, which involves
the identification of a paragraph from existing cases that entails the decision
in a relevant case. We employed the neural models Sentence-BERT and Sent2Vec
for semantic understanding and the traditional retrieval model BM25 for exact
matching in both tasks. As a result, our team ("nigam") ranked 5th among all
the teams in Tasks 1 and 2. Experimental results indicate that the traditional
retrieval model BM25 still outperforms neural network-based models.

利用神经网络和传统检索模型，我们参加了 COLIEE-2022 律师案例比赛的任务 1 和任务 2，结果在所有团队中排名第五。实验结果表明，传统的检索模型 BM25 仍然优于神经网络模型。

基于词汇和语义模型级联的法律案例检索和蕴含

nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models

In the context of the Competition on Legal Information Extraction/Entailment
(COLIEE), we propose a method comprising the necessary steps for finding
relevant documents to a legal question and deciding on textual entailment
evidence to provide a correct answer. The proposed method is based on the
combination of several lexical and morphological characteristics, to build a
language model and a set of features for Machine Learning algorithms. We
provide a detailed study on the proposed method performance and failure cases,
indicating that it is competitive with state-of-the-art approaches on Legal
Information Retrieval and Question Answering, while not needing extensive
training data nor depending on expert produced knowledge. The proposed method
achieved significant results in the competition, indicating a substantial level
of adequacy for the tasks addressed.

本文提出了一种基于语言模型和机器学习方法，结合多种词汇和形态特征，用于解决法律问题和提供正确答案的 Legal Information Extraction/Entailment 问题，且无需大量训练数据和专业知识即可与最先进的法律信息检索和问题回答方式相竞争，并在比赛中获得了显著成果。