Boosting with Subtree - based Decision Stumps and Its application to Semi - strucutred Text Classification

@inproceedings{Kudo2003BoostingWS,
  title={Boosting with Subtree - based Decision Stumps and Its application to Semi - strucutred Text Classification},
  author={Taku Kudo and Yuji Matsumoto},
  year={2003}
}
The research focus in text classification has expanded from a simple topic identification to a more challenging task, such as opinion/modality identification. For the latter, the traditional bag-of-word representations are not sufficient, and a richer, more structural representation will be required. Accordingly, learning algorithms must be able to handle such sub-structures observed in text. In this paper, we propose a Boosting algorithm that captures sub-structures embedded in text. The… CONTINUE READING

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