Discovering Coherent Topics from Urdu Text: A Comparative Study of Statistical Models, Clustering Techniques and Word Embedding
Author:
Affiliation:
1. Central South University,School of Computer Science and Engineering,Changsha,China
2. Hunan University,Department of Computer Science,Changsha,China
3. Ritsumeikan University,Department of Electronic and Computer Engineering,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10227859/10229536/10229612.pdf?arnumber=10229612
Reference22 articles.
1. Reading tea leaves: how humans interpret topic models;boyd-graber;In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems,2009
2. Optimizing semantic coherence in topic models;mimno;EMNLP,2011
3. A framework of Urdu topic modeling using latent dirichlet allocation (LDA)
4. Integrating Document Clustering and Topic Modeling;xie,2013
5. A Comparison of Document Clustering Techniques;steinbach,0
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