Chinese Position Segmentation Based on ALBERT- BiGRU-CRF Model

Abstract

Aiming at the problems of poor parsing effect and poor generalization ability in Chinese position segmentation using current neural network models, this paper proposes a Chinese position segmentation method based on ALBERT-BiGRU-CRF model. The method first uses the ALBERT pre-training model to pre-train the Chinese location information to obtain the context information in all layers, enhance the semantic representation ability of the Chinese location information, and then extract the feature information of the vector through the BiLSTM model, and finally decode it through the CRF model to obtain the global Optimal labeling sequence. Experimental results show that on the basis of different numbers and regions of Chinese location information data sets, the ALBERT-BiGRU-CRF model has better word segmentation accuracy and F1 value on all test sets than the current commonly used neural network models, and the highest can be achieved. 93.91% and 93.96%. Using the ALBERT-BiGRU-CRF model to segment Chinese location information not only effectively improves the accuracy of Chinese location information analysis and polysemous word analysis, but also has better generalization capabilities.