  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.
