Using a smoothing maximum entropy model for chinese nominal entity tagging

Authors:
Jinying Chen;Nianwen Xue;Martha Palmer
Affiliations:
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA;Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA;Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA
Venue:
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Year:
2004

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Abstract

This paper treats nominal entity tagging as a six-way (five categories plus non-entity) classification problem and applies a smoothing maximum entropy (ME) model with a Gaussian prior to a Chinese nominal entity tagging task. The experimental results show that the model performs consistently better than an ME model using a simple count cut-off. The results also suggest that simple semantic features extracted from an electronic dictionary improve the model’s performance, especially when the training data is insufficient.