Abstract
Chinese named entity recognition (CNER) is different from English named entity recognition (ENER). There is no specific delimiter in Chinese text to determine the words in a sentence. Besides, the combination of Chinese text has a strong arbitrariness. These special cases usually bring more errors to the Chinese NER (CNER). We propose a re-ranking model based on BILSTM network and without using any other auxiliary methods. Our approach uses N-best generalised label sequences that are produced by baseline model as input and feeds them into our re-ranking model for modelling the context within the generalised sequences. The optimal output sequence is obtained by comprehensively considering the result of baseline model and re-ranking model. Experimental results show that our model achieves better F1-score on Bakeoff-3 MSRA corpus than the best previous experimental results, which yields a 0.97% improvement on F1-score over our neural baseline model and a 0.22% improvement over the state-of-the-art CNER model.
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