A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to obtain politician profiles which include their interests, and these can be automatically learned from their speeches. As a politician may have various areas of expertise, one alternative is to use a set of subprofiles, each of which covers a different subject. In this study, we propose a novel approach for this task by using latent Dirichlet allocation (LDA) to determine the main underlying topics of each political speech, and to distribute the related terms among the different topic-based subprofiles. With this objective, we propose the use of fifteen distance and similarity measures to automatically determine the optimal number of topics discussed in a document, and to demonstrate that every measure converges into five strategies: Euclidean, Dice, Sorensen, Cosine and Overlap. Our experimental results showed that the scores of the different accuracy metrics of the proposed strategies tended to be higher than those of the baselines for expert recommendation tasks, and that the use of an appropriate number of topics has proved relevant.

在许多政治机构（即议会）中，常见的任务是寻找在特定领域内是专家的政客。为了解决这个问题，第一步是获取包括其兴趣的政治人物简介，并且可以从他们的讲话中自动学习这些兴趣。由于政治家可能具有不同的专业领域，因此一种替代方法是使用一组子配置文件，每个子配置文件涵盖不同的主题。在这项研究中，我们通过使用潜在的狄利克雷分配（LDA）来确定每个政治演讲的主要基础主题，并在不同的基于主题的子配置文件之间分配相关术语，从而提出了一种用于此任务的新颖方法。为此，我们建议使用十五种距离和相似度度量来自动确定文档中讨论的最佳主题数，并证明每种度量都可以归纳为五种策略：欧几里得，骰子，索伦森，余弦和重叠。我们的实验结果表明，所提出策略的不同准确性指标的得分往往高于专家推荐任务的基准得分，并且事实证明，使用适当数量的主题具有相关性。