A Generalized Framework for Revealing Analogous Themes across Related Topics

Zvika Marx, Ido Dagan, Eli Shamir

Abstract: This work addresses the task of identifying thematic correspondences across sub-corpora focused on different topics. We introduce an unsupervised algorithmic framework based on distributional data clustering, which generalizes previous initial works on this task. The empirical results reveal interesting commonalities of different religions. We evaluate the results through measuring the overlap of our clusters with clusters compiled manually by experts. The tested variants of our framework are shown to outperform alternative methods applicable to the task.