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Using Maximum Entropy for Text Classi cation - Kamal Nigam

Using Maximum Entropy for Text Classi cationKamal La ertyyla of Computer ScienceCarnegie Mellon UniversityPittsburgh, PA 15213zJust Research4616 Henry StreetPittsburgh, PA 15213 AbstractThis paper proposes the use of Maximum en-tropy techniques for text Classi cation . Maxi-mum Entropy is a probability distribution esti-mation technique widely used for a variety ofnatural language tasks, such as language mod-eling, part-of-speech tagging, and text segmen-tation. The underlying principle of maximumentropy is that without external knowledge,one should prefer distributions that are uni-form.

Using Maximum Entropy for Text Classi cation Kamal Nigamy knigam@cs.cmu.edu John La ertyy la erty@cs.cmu.edu Andrew McCallumzy mccallum@justresearch.com

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