Japanese named entity recognition based on a simple rule generator and decision tree learning
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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Recognition of Chinese organization names is the key of the recognition of Chinese named entities. However, the lack of a single unified naming system to capture all types of organizations and the uncertainty in word segmentation, make the recognition of Chinese organization names especially difficult. In this paper, we focus on the recognition of Chinese organization names and propose an approach that takes advantage of various types of features of Chinese organization names to address it. First of all, we pre-process inputs to make the recognition more convenient. Secondly, we use the features of the left and right boundary to determine the candidate Chinese organization names automatically. Thirdly, we evaluate and refine the initial recognition results with the features of behaviors and debugging structure patterns to improve the performance of the recognition. From the experimental results on People's Daily testing data set, the approach proposed in this paper outperforms the method based on role tagging more than 7%. And through designing a series of other experiments, we have proved that the proposed approach can perfectly complete the task of recognizing Chinese organization names and is particularly effective in nested cases.