@InProceedings{tan-EtAl:2017:EMNLP2017,
  author    = {Tan, Chuanqi  and  Wei, Furu  and  Ren, Pengjie  and  Lv, Weifeng  and  Zhou, Ming},
  title     = {Entity Linking for Queries by Searching Wikipedia Sentences},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {68--77},
  abstract  = {We present a simple yet effective approach for linking entities in queries. The
	key idea is to search sentences similar to a query from Wikipedia articles and
	directly use the human-annotated entities in the similar sentences as candidate
	entities for the query. Then, we employ a rich set of features, such as
	link-probability, context-matching, word embeddings, and relatedness among
	candidate entities as well as their related entities, to rank the candidates
	under a regression based framework. The advantages of our approach lie in two
	aspects, which contribute to the ranking process and final linking result.
	First, it can greatly reduce the number of candidate entities by filtering out
	irrelevant entities with the words in the query. Second, we can obtain the
	query sensitive prior probability in addition to the static link-probability
	derived from all Wikipedia articles. We conduct experiments on two benchmark
	datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
	dataset. Experimental results show that our method outperforms state-of-the-art
	systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
	dataset.},
  url       = {https://www.aclweb.org/anthology/D17-1007}
}