@InProceedings{shi-EtAl:2016:OKBQA2016,
  author    = {Shi, Jing  and  Xu, Jiaming  and  Yao, Yiqun  and  Zheng, Suncong  and  Xu, Bo},
  title     = {Combining Lexical and Semantic-based Features for Answer Sentence Selection},
  booktitle = {Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {30--38},
  abstract  = {Question answering is always an attractive and challenging task in natural
	language processing area. There are some open domain question answering
	systems, such as IBM Waston, which take the unstructured text data as input, in
	some ways of humanlike thinking process and a mode of artificial intelligence.
	At the conference on Natural Language Processing and Chinese Computing~(NLPCC)
	2016, China Computer Federation hosted a shared task evaluation about Open
	Domain Question Answering. We achieve the 2nd place at the document-based
	subtask. In this paper, we present our solution, which consists of feature
	engineering in lexical and semantic aspects and model training methods. As the
	result of the evaluation shows, our solution provides a valuable and brief
	model which could be used in modelling question answering or sentence semantic
	relevance. We hope our solution would contribute to this vast and significant
	task with some heuristic thinking.},
  url       = {http://aclweb.org/anthology/W16-4404}
}