@InProceedings{guo-EtAl:2017:EACLlong,
  author    = {Guo, Shangmin  and  Zeng, Xiangrong  and  He, Shizhu  and  Liu, Kang  and  Zhao, Jun},
  title     = {Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {111--120},
  abstract  = {As one of the most important test of China, Gaokao is designed to be difficult
	enough to distinguish the excellent high school students. In this work, we
	detailed the Gaokao History Multiple Choice Questions(GKHMC) and proposed two
	different approaches to address them using various resources. One approach is
	based on entity search technique (IR approach), the other is based on text
	entailment approach where we specifically employ deep neural networks(NN
	approach). The result of experiment on our collected real Gaokao questions
	showed that they are good at different categories of questions, that is IR
	approach performs much better at entity questions(EQs) while NN approach shows
	its advantage on sentence questions(SQs). We achieve state-of-the-art
	performance and show that it's indispensable to apply hybrid method when
	participating in the real-world tests.},
  url       = {http://www.aclweb.org/anthology/E17-1011}
}