@InProceedings{li-EtAl:2016:COLING1,
  author    = {Li, Ximing  and  Chi, Jinjin  and  Li, Changchun  and  Ouyang, Jihong  and  Fu, Bo},
  title     = {Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs},
  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     = {151--160},
  abstract  = {Gaussian LDA integrates topic modeling with word embeddings by replacing
	discrete topic distribution over word types with multivariate Gaussian
	distribution on the embedding space. This can take semantic information of
	words into account. However, the Euclidean similarity used in Gaussian topics
	is not an optimal semantic measure for word embeddings. Acknowledgedly, the
	cosine similarity better describes the semantic relatedness between word
	embeddings. To employ the cosine measure and capture complex topic structure,
	we use von Mises-Fisher (vMF) mixture models to represent topics, and then
	develop a novel mix-vMF topic model (MvTM). Using public pre-trained word
	embeddings, we evaluate MvTM on three real-world data sets. Experimental
	results show that our model can discover more coherent topics than the
	state-of-the-art baseline models, and achieve competitive classification
	performance.},
  url       = {http://aclweb.org/anthology/C16-1015}
}