@InProceedings{mao-EtAl:2016:COLING,
  author    = {Mao, Xian-Ling  and  Hao, Yi-Jing  and  Zhou, Qiang  and  Yuan, Wen-Qing  and  Yang, Liner  and  Huang, Heyan},
  title     = {A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing},
  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     = {3339--3348},
  abstract  = {Recently, topic modeling has been widely applied in data mining due to its
	powerful ability. A common, major challenge in applying such topic models to
	other tasks is to accurately interpret the meaning of each topic. Topic
	labeling, as a major interpreting method, has attracted significant attention
	recently. However, most of previous works only focus on the effectiveness of
	topic labeling, and less attention has been paid to quickly creating good topic
	descriptors; meanwhile, it’s hard to assign labels for new emerging topics by
	using most of existing methods. To solve the problems above, in this paper, we
	propose a novel fast topic labeling framework that casts the labeling problem
	as a k-nearest neighbor (KNN) search problem in a probability vector set. Our
	experimental results show that the proposed sequential interleaving method
	based on locality sensitive hashing (LSH) technology is efficient in boosting
	the comparison speed among probability distributions, and the proposed
	framework can generate meaningful labels to interpret topics, including new
	emerging topics.},
  url       = {http://aclweb.org/anthology/C16-1315}
}