Citation

Murata M, Uchimoto K, Utiyama M, Ma Q, Nishimura R, Watanabe Y, Doi K, Torisawa K. Cogn. Comput. 2010; 2(4): 272-279.

Copyright

(Copyright © 2010, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12559-010-9046-3

PMID

unavailable

Abstract

The maximum entropy (ME) method is a powerful supervised machine learning technique that is useful for various tasks. In this paper, we introduce new studies that successfully employ ME for natural language processing (NLP) problems including machine translation and information extraction. Specifically, we demonstrate, using simulation results, three applications of ME for NLP: estimation of categories, extraction of important features, and correction of error data items. We also evaluate the comparative performance of the proposed ME methods with other state-of-the-art approaches.

Language: en