F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media

Understand

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.