Using Maximum Entropy for Text Classi cation - …
Using Maximum Entropy for Text Classi cationKamal La ertyyla of Computer ScienceCarnegie Mellon UniversityPittsburgh, PA 15213zJust Research4616 Henry StreetPittsburgh, PA 15213AbstractThis 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.
the likelihood, then we know it will converge to the glob-ally optimal set of parameters|those that are both the maximum likelihood solution for …
Download Using Maximum Entropy for Text Classi cation - …
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Models for Survival Analysis with Covariates, Maximum likelihood estimation of mean reverting, Maximum likelihood estimation of mean reverting processes, Gaussian, Handling Missing Data by Maximum, Handling Missing Data by Maximum Likelihood, Chapter 2: Maximum Likelihood Estimation, Maximum Likelihood, LOPA Articles, Maximum Likelihood Estimation of an, Maximum Likelihood Estimation and Nonlinear, Maximum