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

LDA-based Term Profiles for Expert Finding in a Political Setting