  We focus on named entity recognition (NER) for Chinese social media. With
massive unlabeled text and quite limited labelled corpus, we propose a
semi-supervised learning model based on B-LSTM neural network. To take
advantage of traditional methods in NER such as CRF, we combine transition
probability with deep learning in our model. To bridge the gap between label
accuracy and F-score of NER, we construct a model which can be directly trained
on F-score. When considering the instability of F-score driven method and
meaningful information provided by label accuracy, we propose an integrated
method to train on both F-score and label accuracy. Our integrated model yields
7.44\% improvement over previous state-of-the-art result.
