Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fields
This work addresses structured learning challenges in supervised machine learning, specifically for document understanding, but appears incremental as it extends existing CRF methods.
The paper tackles the problem of joint structured learning for interdependent objects and enforcing logical constraints in predictions within Conditional Random Fields, applied to a Document Understanding task, and reports performance evaluation on a public dataset.
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.