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.
翻译：在社区问题解答(QA)系统中,各领域的专家调查结果都得到了很好的研究,然而,这些研究没有一项涉及法律领域的专家调查结果,而法律领域的目标是让公民根据自己的专长找到律师。在法律领域,专家和搜索者之间存在巨大的知识差距,法律质量解答网站的内容包括正式和非正式的交流。我们在本文件中提出了为律师编制基于查询的文本简介的方法,涉及包括情感、评论和正确性在内的若干方面。我们把基于查询的简介与现有的专家调查结果方法结合起来。我们实验的是一个从在线法律质询服务中收集的新数据集。我们发现,考虑到不同的律师概况,改进了最佳基线模式。我们为今后的工作公开提供我们的数据。
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