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Chinese named entity recognition (CNER) is an important task in Chinesenatural language processing field. However, CNER is very challenging sinceChinese entity names are highly context-dependent. In addition, Chinese textslack delimiters to separate words, making it difficult to identify the boundaryof entities. Besides, the training data for CNER in many domains is usuallyinsufficient, and annotating enough training data for CNER is very expensiveand time-consuming. In this paper, we propose a neural approach for CNER.First, we introduce a CNN-LSTM-CRF neural architecture to capture both localand long-distance contexts for CNER. Second, we propose a unified framework tojointly train CNER and word segmentation models in order to enhance the abilityof CNER model in identifying entity boundaries. Third, we introduce anautomatic method to generate pseudo labeled samples from existing labeled datawhich can enrich the training data. Experiments on two benchmark datasets showthat our approach can effectively improve the performance of Chinese namedentity recognition, especially when training data is insufficient.

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation