Systems and methods for narrative detection and frame detection using generalized concepts and relations

Abstract

Co-clustering based on generalized conceptual relationships can automatically detect story forms incorporating archetypes/targets and actions. Co-clustering can help in identifying similarities that exist in low-dimensional sub-spaces of sparse data such as textual paragraphs. Through co-clustering, the clusters themselves and their characteristic features are identifiable which can be useful in describing and summarizing their contents. The residual error of factorization with concept-based features is significantly lower than the error with prior keyword-based features. Qualitative evaluations also suggest that concept-based features yield more coherent, distinctive and interesting story forms compared to those produced by using prior keyword-based features.