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Using the Maximum Entropy Method for Natural Language Processing: Category Estimation, Feature Extraction, and Error Correction
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DOI：10.1007/s12559-010-9046-3.png)
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Abstract
En 中文
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.
Keywords:
Maximum entropy
Category estimation
Important feature
Error correction
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