Topic2Vec: Learning distributed representations of topics

@article{Niu2015Topic2VecLD,
  title={Topic2Vec: Learning distributed representations of topics},
  author={Liqiang Niu and Xin-Yu Dai},
  journal={2015 International Conference on Asian Language Processing (IALP)},
  year={2015},
  pages={193-196}
}
Latent Dirichlet Allocation (LDA) mining thematic structure of documents plays an important role in nature language processing and machine learning areas. However, the probability distribution from LDA only describes the statistical relationship of occurrences in the corpus and usually in practice, probability is not the best choice for feature representations. Recently, embedding methods have been proposed to represent words and documents by learning essential concepts and representations… CONTINUE READING
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