Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields

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 constraints when predicting labels. We explain how this need arose in a Document Understanding task. We then discuss a general extension to Conditional Random Fields (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. Keywords: supervised machine learning, structured prediction, conditional random fields.