Multi-label text classification with Probabilistic Topic Model ml-PLSI
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Karpovich S. N. Multi-label text classification with Probabilistic Topic Model ml-PLSI.

Keywords: Multi-label classification, supervised learning, topic model, natural language processing.

SUMMARY The paper proposes a method of multi-label classification for documents with topic model. A lot of researches of clustering and classification algorithms have one label for one document when one document can be relevant to several labels. The task is very actual. A comparative analysis of algorithms for multi-label classification is made. The article describes technology tools for the multi-label classification algorithm. A Topic Model is created by a supervised learning. We have estimated the classification quality and made a list of proposed categories for a word. The developed approach has shown its efficiency. Probabilistic estimations of the assignment of a document to a category allow to use it in the collective recognition and associative classification. Further we will research the opportunities of multi-label classification with probabilistic topic model.


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