@InProceedings{yin-EtAl:2016:COLING,
  author    = {Yin, Wenpeng  and  Yu, Mo  and  Xiang, Bing  and  Zhou, Bowen  and  Sch\"{u}tze, Hinrich},
  title     = {Simple Question Answering by Attentive Convolutional Neural Network},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1746--1756},
  abstract  = {This work focuses on answering single-relation factoid questions over Freebase.
	Each question can acquire the answer from a single fact of form (subject,
	predicate, object) in Freebase.  This task, simple question answering
	(SimpleQA), can be addressed via a two-step pipeline: entity linking and fact
	selection. In fact selection, we match the subject entity in a fact candidate
	with the entity mention in the question by a character-level convolutional
	neural network (char-CNN), and match the predicate in that fact with the
	question by a word-level CNN (word-CNN). This work makes two main
	contributions. (i) A simple and effective entity linker over Freebase is
	proposed. Our entity linker outperforms the
	state-of-the-art entity linker over SimpleQA task. (ii) A novel attentive
	maxpooling is stacked over word-CNN, so that the predicate representation can
	be matched with the predicate-focused question representation more effectively.
	Experiments show that our system sets new state-of-the-art in this task.},
  url       = {http://aclweb.org/anthology/C16-1164}
}