Corpus ID: 15240372

Topic Modeling Using Distributed Word Embeddings

@article{Randhawa2016TopicMU,
  title={Topic Modeling Using Distributed Word Embeddings},
  author={R. Randhawa and Parag Jain and G. Madan},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.04747}
}
We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised and generates a list of topics ranked with respect to importance. We find that it works better than existing topic modeling techniques such as Latent Dirichlet Allocation for identifying key topics in user-generated content, such as emails, chats, etc., where topics are diffused… Expand

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