Multi Feature Space Combination for Authorship Clustering

The Author Identification task for PAN 2016 consisted of three different Sub-tasks: authorship clustering, authorship links and author diarization. We developed a machine learning approaches for two of three of these tasks. For the two authorship related tasks we created various sets of feature spaces. The challenge was to combine these feature spaces to enable the machine learning algorithms to detect these difference authors across multiple feature spaces. In the case of authorship clustering we combine these feature spaces and use a two-step approach for clustering. Then we use results of the clustering, and employ new feature space to determine links between documents in given problems.