Abstract

This paper introduces coupled clustering—a novel computational framework for detecting corresponding themes in unstructured data. Gaining its inspiration from the structure mapping theory, our framework utilizes unsupervised statistical learning tools for automatic construction of aligned representations reflecting the context of the particular mapping being made. The coupled clustering algorithm is demonstrated and evaluated through detecting conceptual correspondences in textual corpora. In its current phase, the method is primarily oriented towards context-dependent feature-based similarity. However, it is preliminary demonstrated how it could be utilized for identification of relational commonalities, as well