论文标题
判例法检索：过去20年中的问题，方法，挑战和评估
Case law retrieval: problems, methods, challenges and evaluations in the last 20 years
论文作者
论文摘要
判例法的检索是检索与法律问题有关的司法裁决。判例法检索包括大量律师的时间，对于确保准确的建议和减少工作量很重要。我们从过去20年开始调查判例法检索的方法，并概述了对判例法检索系统评估所面临的问题和挑战。有限的已发表工作重点是改善临时判例法检索的排名。但是，在判例法检索的其他领域和法律信息检索中也进行了重大工作。这很可能是由于合法搜索提供者不愿意放弃其成功的秘密。大多数对判例法检索的评估都是在小型藏品上进行的，并专注于相关任务，例如提问系统或推荐系统。工作并未集中在Cranfield风格的评估和判例法检索方法的基础上，并不存在公开可用的测试集。这面临着一个重大挑战。但是，至少在商业环境中，有理由质疑这个问题的程度。如果没有测试收集到基线方法，就无法知道方法是否有希望。商业法律搜索提供商的作品展示了自然语言系统的有效性以及查询判例法检索的扩展。机器学习正在应用于越来越多的法律搜索任务，这无疑代表了判例法检索的未来。
Case law retrieval is the retrieval of judicial decisions relevant to a legal question. Case law retrieval comprises a significant amount of a lawyer's time, and is important to ensure accurate advice and reduce workload. We survey methods for case law retrieval from the past 20 years and outline the problems and challenges facing evaluation of case law retrieval systems going forward. Limited published work has focused on improving ranking in ad-hoc case law retrieval. But there has been significant work in other areas of case law retrieval, and legal information retrieval generally. This is likely due to legal search providers being unwilling to give up the secrets of their success to competitors. Most evaluations of case law retrieval have been undertaken on small collections and focus on related tasks such as question-answer systems or recommender systems. Work has not focused on Cranfield style evaluations and baselines of methods for case law retrieval on publicly available test collections are not present. This presents a major challenge going forward. But there are reasons to question the extent of this problem, at least in a commercial setting. Without test collections to baseline approaches it cannot be known whether methods are promising. Works by commercial legal search providers show the effectiveness of natural language systems as well as query expansion for case law retrieval. Machine learning is being applied to more and more legal search tasks, and undoubtedly this represents the future of case law retrieval.
