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Bibliographie

  1. Latent Dirichlet allocation

    http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation

  2. What papers have shown that for machine learning, data set size is more important than the model being trained?

    http://www.quora.com/Machine-Learning/What-papers-have-shown-that-for-machine-learning-data-set-size-is-more-important-than-the-model-being-trained

  3. Lessons learned developing a practical large scale machine learning system

    http://googleresearch.blogspot.com/2010/04/lessons-learned-developing-practical.html

  4. De la classification d'opinions à la recommdantion : l'apport des textes communautiares

    http://www.atala.org/IMG/pdf/1-Poirier-TAL51-3.pdf

  5. Clustering Short Texts using Wikipedia

    http://www.hpl.hp.com/india/documents/papers/clusteringshorttexts.pdf

    Short Text Classification in Twitter to Improve Information Filtering

    http://www.hpl.hp.com/india/documents/papers/clusteringshorttexts.pdf

  6. Global vs. Local Graph Ranking

    http://markorodriguez.com/2011/03/30/global-vs-local-graph-ranking/

  7. Rational Research Model for Ranking Semantic Entities

    http://baggins.nottingham.edu.my/~wangwei/publications/rarerank.pdf