To further improve existing named entity recognition models which are defected in weak generalization a-bility and poor robustness,this paper proposed to optimize the hyperparameters of the bi-directional long and short-term memory network(BiLSTM)using the multi-strategy slime mould algorithm(SLSMA).The improved slime mould algorithm adopts the Sobol sequence uniform population density in the initialization stage,and introduces the Levy flight strategy to dynamically adjust the step size in the late iteration stage to enable the algorithm jump out of the local optimum.The improved slime mould algorithm is also applied to optimize the key hyper parameters for named entity recognition.Experiments of named entity recognition using LSTM-CRF model,BiLSTM-CRF model,SMA-BiLSTM-CRF model and SLSMA-BiLSTM-CRF model show that the values of SLSMA-BiLSTM-CRF reach 98.48％and 97.35％on the People's Daily and CoNLL2003 datasets,respectively.
关键词
命名实体识别/SLSMA/双向长短时记忆网络/Sobol序列
Key words
named entity recognition/SLSMA/Bidirectional long short-term memory network/Sobol sequence
维普
万方数据