@InProceedings{xu-EtAl:2016:COLING3,
  author    = {xu, kun  and  Feng, Yansong  and  Huang, Songfang  and  Zhao, Dongyan},
  title     = {Hybrid Question Answering over Knowledge Base and Free Text},
  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     = {2397--2407},
  abstract  = {Recent trend in question answering (QA) systems focuses on using structured
	knowledge bases (KBs) to find answers. While these systems are able to provide
	more precise answers than information retrieval (IR) based QA systems, the
	natural incompleteness of KB inevitably limits the question scope that the
	system can answer. In this paper, we present a hybrid question answering
	(hybrid-QA) system which exploits both structured knowledge base and free text
	to answer a question.
	The main challenge is to recognize the meaning of a question using these two
	resources, i.e., structured KB and free text. To address this, we map
	relational phrases to KB predicates and textual relations simultaneously, and
	further develop an integer linear program (ILP) model to infer on these
	candidates and provide a globally optimal solution.
	Experiments on benchmark datasets show that our system can benefit from both
	structured KB and free text, outperforming the state-of-the-art systems.},
  url       = {http://aclweb.org/anthology/C16-1226}
}