@InProceedings{he-sun:2017:EACLshort,
  author    = {He, Hangfeng  and  Sun, Xu},
  title     = {F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {713--718},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/E17-2113}
}