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A Novel Dynamic Adaptive Method Based on Artificial Immune System in Chinese Named Entity Recognition


Wei Jiang, Yi Guan, XiaoLong Wang


Vol. 6  No. 4  pp. 1-8


Named Entity Recognition(NER), as a task of providing important semantic information, is a critical first step in information Extraction and Question Answer system. The NER has been proved to be NP-hard problem, and the existing methods usually adopt supervised or unsupervised learning model, as a result, there is still a distance away from the required performance in real application, however the system can hardly improved with the model being applied. The paper proposes a novel method based on artificial immune system(AIS) for NER. We apply clonal selection principle and affinity maturation of the vertebrate immune response, where the secondary immune response has high performance than the primary immune response, and the similar antigens may have a good immunity. We also introduce the reinforcement learning method into our system to tune the immune response, and the context features are exploited by the maximum entropy principle. The Experimental result indicate that our method exhibits a good performance and implements the dynamic learning behavior.


Named Entity Recognition, Artificial Immme System, Maximum Entropy Principle, Reinforcement Learning, QA System