@InProceedings{andy-EtAl:2016:WNUT,
  author    = {Andy, Anietie  and  Sekine, Satoshi  and  Rwebangira, Mugizi  and  Dredze, Mark},
  title     = {Name Variation in Community Question Answering Systems},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {51--60},
  abstract  = {Name Variation in Community Question Answering Systems
	                                Abstract
	Community question answering systems are forums where users can ask and answer
	questions in various categories. Examples are Yahoo! Answers, Quora, and Stack
	Overflow. A common challenge with such systems is that a significant percentage
	of asked questions are left unanswered. In this paper, we propose an algorithm
	to reduce the number of unanswered questions in Yahoo! Answers by reusing the
	answer to the most similar past resolved question to the unanswered question,
	from the site. Semantically similar questions could be worded differently,
	thereby making it difficult to find questions that have shared needs. For
	example, "Who is the best player for the Reds?" and "Who is currently the
	biggest star at Manchester United?" have a shared need but
	are worded differently; also, "Reds" and "Manchester United" are used to refer
	to the soccer team Manchester United football club. In this research, we focus
	on question categories that contain a large number of named entities and entity
	name variations. We show that in these categories, entity linking can be used
	to identify relevant past resolved questions with shared needs as a given
	question by disambiguating named entities and matching these questions based on
	the disambiguated entities, identified entities, and knowledge base information
	related to these entities. We evaluated our algorithm on a new dataset
	constructed from Yahoo! Answers. The dataset contains annotated question pairs,
	(Qgiven, [Qpast, Answer]). We carried out experiments on several question
	categories and show that an entity-based approach gives good performance when
	searching for similar questions in entity rich categories.},
  url       = {http://aclweb.org/anthology/W16-3909}
}