@InProceedings{masumura-EtAl:2017:I17-2,
  author    = {Masumura, Ryo  and  Asami, Taichi  and  Masataki, Hirokazu  and  Sadamitsu, Kugatsu  and  Nishida, Kyosuke  and  Higashinaka, Ryuichiro},
  title     = {Hyperspherical Query Likelihood Models with Word Embeddings},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {210--216},
  abstract  = {This paper presents an initial study on hyperspherical query likelihood models
	(QLMs) for information retrieval (IR). Our motivation is to naturally utilize
	pre-trained word embeddings for probabilistic IR. To this end, key idea is to
	directly leverage the word embeddings as random variables for directional
	probabilistic models based on von Mises-Fisher distributions which are familiar
	to cosine distances. The proposed method enables us to theoretically take
	semantic similarities between document and target queries into consideration
	without introducing heuristic expansion techniques. In addition, this paper
	reveals relationships between hyperspherical QLMs and conventional QLMs.
	Experiments show document retrieval evaluation results in which a
	hyperspherical QLM is compared to conventional QLMs and document distance
	metrics using word or document embeddings.},
  url       = {http://www.aclweb.org/anthology/I17-2036}
}