Transcription of Using Maximum Entropy for Text Classi cation - Kamal …
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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. Constraints on the distribution, derivedfrom labeled training data, inform the tech-nique where to be minimally non-uniform.
conditional distributions. Then, the speci c application of maximum entropy to text classi cation is discussed in Section 3. Related work is presented in Section 4.
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