F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media
Hangfeng He, Xu Sun · 2017
We focus on named entity recognition (NER) for Chinese social media.With massive unlabeled text and quite limited labelled corpus, we propose a semisupervised 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 Fscore 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 substantial improvement over previous state-of-the-art result.