  Expert finding has been well-studied in community question answering (QA)
systems in various domains. However, none of these studies addresses expert
finding in the legal domain, where the goal is for citizens to find lawyers
based on their expertise. In the legal domain, there is a large knowledge gap
between the experts and the searchers, and the content on the legal QA websites
consist of a combination formal and informal communication. In this paper, we
propose methods for generating query-dependent textual profiles for lawyers
covering several aspects including sentiment, comments, and recency. We combine
query-dependent profiles with existing expert finding methods. Our experiments
are conducted on a novel dataset gathered from an online legal QA service. We
discovered that taking into account different lawyer profile aspects improves
the best baseline model. We make our dataset publicly available for future
work.
