Learning Soft Linear Constraints with Application to Citation Field Extraction

Understand

Accurately segmenting a citation string into fields for authors, titles, etc.

• is a challenging task because the output typically obeys various global constraints.
• Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance.
• On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference.