Recent improvements to NeuroCRFs for named entity recognition

We present improvements to NeuroCRFs, a combination of neural network (NN) and conditional random fields (CRF) used for sequence labelling. The NN component is used to provide feature for label transitions that are then used by the CRF component to compute the likelihood. By exploiting the similarities between labels, we were able to add parameters shared by groups of similar label transitions. We also investigated large margin training, which increases the log-likelihood of the correct hypothesis relative to the best competing hypothesis. Finally, we used ensemble learning to combine the models trained from multiple initializations. Using a combination of those approach, we obtain F₁ = 88.50, a significant improvement over the 87.49 baseline on a named entities recognition task.