Unsupervised extration of templates from texts

Templates are multi-word expressions with slots, such as “Starting at _ on _ ” or “regard _ as _”, that appear frequently in text and also in data from sources such as Twitter. Automatic extraction of these template expressions is a challenging problem that is related to grammar inference. We propose automatic template extraction by using a binary decision diagram (BDD), which is mathematically equivalent to a minimal deterministic finite-state automaton (DFA). We have studied a basic formulation and currently seek a larger application to extract patterns from social networking service (SNS) data through additional use of deep learning methods.