Chinese Named Entity Recognition with the Improved Smoothed Conditional Random Fields

As a kind of state-of-the-art sequence classifier, Conditional Random Fields (CRFs) recently have been widely used for some natural language processing tasks which could be viewed as the sequence labeling problems such as POS tagging, named entity recognition(NER) etc. But CRFs suffer from the failing that they are prone to overfitting when the number of features grows. For NER task, the feature set is very large, especially for Chinese language, because of it’s complex characteristics. Existing approaches to avoid overfitting include the regularization and feature selection. The main shortcoming of these approaches is that they ignore the so-called unsupported features which are the features appearing in the test set but with zero count in the training set. Actually, without the information of them, the generalization of the CRFs suffers. This paper describes a model called Improved Smoothed CRF which could capture the information of the unsupported features using the smoothing features. It provides a very effective and practical way to improve the generalization performance of CRFs. Experiments on Chinese NER proved the effectiveness of our method.