In addition, any machine learning efforts towards event factuality identification can both train over De Facto's output, as well as benefit from the lexical types and syntactic features it uses when considering options for machine learning algorithm choice and feature engineering decisions.
we believe that the linguistically motivated model we propose here can, in addition to provide actual information on natural language text, help us understand the phenomenon of event factuality and complement data-driven approaches commonly used in the field.