Argument mining research has studied different data-driven approaches for separating organizational content (shell) from topical content to improve argument component identification, e.g., supervised sequence model (Madnani et al., 2012), unsupervised probabilistic topic models (Séaghdha and Teufel, 2014;Du et al., 2014).
the authors first use topic prompts in development data of unannotated persuasive essays to semi-automatically collect argument and domain seed words.