Affiliations: College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu Province, China
Correspondence:
[*]
Corresponding author. Yun Zhang, College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu Province, China. E-mail: [email protected].
Abstract: The lack of training data in new domain is a typical problem for named entity recognition (NER). Currently, researchers have introduced “entity trigger” to improve the cost-effectiveness of the model. However, it still required the annotator to attach additional trigger label, which increases the workload of the annotator. Moreover, this trigger applies only to English text and lacks research into other languages. Based on this problem, we have proposed a more cost-effective trigger tagging method and matching network. The approach not only automatic tagging entity triggers based on the characteristics of Chinese text, but also adds mogrifier LSTM to the matching network to reduce context-free representation of input tokens. Experiments on two public datasets show that our automatic trigger is effective. And it achieves better performances with automatic trigger than other state-of-the-art methods (The F1-scores increased by 1∼4).
Keywords: Chinese NER, entity trigger, Mogrifier LSTM, TMN, m-TMN
