Using a Smoothing Maximum Entropy Model for Chinese Nominal Entity Tagging

This paper treats nominal entity tagging as a six-way (five categories plus nonentity) classification problem and applies a smoothing maximum entropy (ME) model with a Gaussian prior to the Chinese nominal entity tagging task. The experimental results show that the model performs consistently better than a ME model using a simple counting 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.