Chinese Named Entity Recognition with Multiple Features

This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model integrates coarse particle feature (POS Model) with fine particle feature (Word Model), so that it can overcome the disadvantages of each other. Secondly, in order to reduce the searching space and improve the efficiency, we introduce heuristic human knowledge into statistical model, which could increase the performance of NER significantly. Thirdly, we use three sub-models to respectively describe three kinds of transliterated person name, that is, Japanese, Russian and Euramerican person name, which can improve the performance of PN recognition. From the experimental results on People's Daily testing data, we can conclude that our Hybrid Model is better than the models which only use one kind of features. And the experiments on MET-2 testing data also confirm the above conclusion, which show that our algorithm has consistence on different testing data.