Corpus ID: 32288359

Topic supervised non-negative matrix factorization

@article{MacMillan2017TopicSN,
  title={Topic supervised non-negative matrix factorization},
  author={Kelsey MacMillan and James D. Wilson},
  journal={ArXiv},
  year={2017},
  volume={abs/1706.05084}
}
Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs. test set evaluations, it is often the case that the results of a topic model do not align with human interpretation. This interpretability fallacy is largely due to the unsupervised nature of topic models, which prohibits any user guidance on the results of a model. In this paper, we introduce a semi… Expand
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