This paper addresses the semi-automatic annotation of subjects, also called policy areas, in the Danish Parliament Corpus (2009-2017)
v.2. Recently, the corpus has been made available through the CLARIN-DK repository, the Danish node of the European CLARIN
infrastructure. The paper also contains an analysis of the subjects in the corpus, and a description of multi-label classification
experiments act to verify the consistency of the subject annotation and the utility of the corpus for training classifiers on this type of
data. The analysis of the corpus comprises an investigation of how often the parliament members addressed each subject and the relation between subjects and gender of the speaker. The classification experiments show that classifiers can determine the two co-occurring subjects of the speeches from the agenda titles with a performance similar to that of human annotators. Moreover, a multilayer perceptron achieved an F1-score of 0.68 on the same task when trained on bag of words vectors obtained from the speeches’ lemmas. This is an improvement of more than 0.6 with respect to the baseline, a majority classifier that accounts for the frequency of the classes. The result is promising given the high number of subject combinations (186) and the skewness of the data.