@InProceedings{kano:2016:OKBQA2016,
  author    = {Kano, Yoshinobu},
  title     = {Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations},
  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     = {91--96},
  abstract  = {Answering yes--no questions is more difficult than simply retrieving ranked
	search results. To answer yes--no questions, especially when the correct
	answer is no, one must find an objectionable keyword that makes the question's
	answer no. Existing systems, such as factoid-based ones, cannot answer yes--no
	questions very well because of insufficient handling of such objectionable
	keywords. We suggest an algorithm that answers yes--no questions by assigning
	an importance to objectionable keywords. Concretely speaking, we suggest a
	penalized scoring method that finds and makes lower score for parts of
	documents that include such objectionable keywords. We check a keyword
	distribution for each part of a document such as a paragraph, calculating the
	keyword density as a basic score. Then we use an objectionable keyword penalty
	when a keyword does not appear in a target part but appears in other parts of
	the document. Our algorithm is robust for open domain problems because it
	requires no training. We achieved 4.45 point better results in F1 scores than
	the best score of the NTCIR-10 RITE2 shared task, also obtained the best score
	in 2014 mock university examination challenge of the Todai Robot project.},
  url       = {http://aclweb.org/anthology/W16-4413}
}