  This paper is concerned with structured machine learning, in a supervised
machine learning context. It discusses how to make joint structured learning on
interdependent objects of different nature, as well as how to enforce logical
con-straints when predicting labels. We explain how this need arose in a
Document Understanding task. We then discuss a general extension to Conditional
Random Field (CRF) for this purpose and present the contributed open source
implementation on top of the open source PyStruct library. We evaluate its
performance on a publicly available dataset.
