A New Recurrent Neural CRF for Learning Non-linear Edge Features

Understand

Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction.

• More recently, there is a marriage of CRF and recurrent neural models, so that we can gain from both non-linear dense features and globally normalized CRF objective.
• These recurrent neural CRF models mainly focus on encode node features in CRF undirected graphs.
• However, edge features prove important to CRF in structured prediction.