A model for handling approximate, noisy or incomplete labeling in text classification

@inproceedings{Ramakrishnan2005AMF,
  title={A model for handling approximate, noisy or incomplete labeling in text classification},
  author={Ganesh Ramakrishnan and Krishna Prasad Chitrapura and Raghu Krishnapuram and Pushpak Bhattacharyya},
  booktitle={ICML},
  year={2005}
}
We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties associated with the labeling process. Given a labeled or partially labeled training corpus of text documents, the model estimates the joint distribution of training documents and class labels by using a generalization of the Expectation Maximization algorithm. The estimates can be used in standard classification models to reduce error rates. Since uncertainties in the labeling are taken into account, the model… CONTINUE READING
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