论文标题
法律社区问题的专家发现回答
Expert Finding in Legal Community Question Answering
论文作者
论文摘要
专家发现在各个领域的社区问答系统（QA）系统中得到了很好的研究。但是，这些研究都没有针对法律领域的专家发现，在这种发现中，公民的目标是根据其专业知识找到律师。在法律领域，专家和搜索者之间存在很大的知识差距，法律质量检查网站上的内容包括正式和非正式的沟通组合。在本文中，我们提出了为律师生成依赖查询的文本配置文件的方法，这些律师涵盖了几个方面，包括情感，评论和新近度。我们将依赖查询的配置文件与现有的专家发现方法相结合。我们的实验是在从在线法律质量检查服务中收集的新型数据集上进行的。我们发现，考虑到不同的律师资料方面，可以改善最佳基线模型。我们将数据集公开用于将来的工作。
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.
