To avoid the error propagation of pipeline methods, some studies use joint learning based on traditional machine learning algorithms and hand-crafted features, including Imperatively Defined Factor graph (IDF) (Klinger and Cimiano, 2013a), joint inference based on IDF (Klinger and Cimiano, 2013b), and Integer Linear Programming (ILP) (Yang and Cardie, 2013).
these methods heavily depend on the quality of handcrafted features and sometimes perform worse than pipeline methods (Klinger and Cimiano, 2013b).