@InProceedings{zhao-EtAl:2018:C18-1,
  author    = {Zhao, Jianyu  and  Zhan, Zhiqiang  and  Yang, Qichuan  and  Zhang, Yang  and  Hu, Changjian  and  Li, Zhensheng  and  Zhang, Liuxin  and  He, Zhiqiang},
  title     = {Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {2033--2043},
  abstract  = {Representation learning is a key issue for most Natural Language Processing (NLP) tasks. Most existing representation models either learn little structure information or just rely on pre-defined structures, leading to degradation of performance and generalization capability.},
  url       = {http://www.aclweb.org/anthology/C18-1173}
}