Gated Recursive Neural Network for Chinese Word Segmentation

2015 ◽  
Author(s):  
Xinchi Chen ◽  
Xipeng Qiu ◽  
Chenxi Zhu ◽  
Xuanjing Huang
Keyword(s):  
Neural Network ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Recursive Neural Network

An improved neural network for domain adaptive Chinese word segmentation

2020 ◽  
pp. 1-11
Author(s):  
Ming Jiang ◽  
Tao Huang ◽  
Min Zhang ◽  
Jingfan Tang ◽  
Zhiyong Liu
Keyword(s):  
Neural Network ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation

Chinese Word Segmentation Based on the First Kind of Spline Weight Function Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 683-686 ◽  
Author(s):  
Dai Yuan Zhang ◽  
Yan Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Technology ◽  
Weight Function ◽  
English Word ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Continuous Development ◽  
Chinese Writing

With the continuous development of information technology, word segmentation technology becomes an important link in dealing with the increasing amount of information conveniently. Different from English word segmented by spaces, Chinese writing is continuous, and there is no space between words, this brings a lot of trouble to word segmentation. In this article, through the analysis of different property of words to get a code that can be used for training and combined it with the first kind of spline weight function neural network, then by training a large number of existing rules encoding to generate a study method that can divide the statement correctly.

Learning Chinese Word Segmentation Based on Bidirectional GRU-CRF and CNN Network Model

2019 ◽  
Vol 15 (3) ◽  
pp. 47-62 ◽  
Author(s):  
Chenghai Yu ◽  
Shupei Wang ◽  
Jiajun Guo
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Neural Network Models ◽  
Long Distance ◽  
Training Time

Chinese word segmentation is the basis of the Chinese natural language processing (NLP). With the development of the deep learning, various neural network models are applied to the Chinese word segmentation. However, current neural network models have the characteristics of artificial feature extraction, nonstandard word-weight, inability to effectively use long-distance information and long training time of models in Chinese word segmentation. To solve a series of problems, this article presents a CNN-Bidirectional GRU-CRF neural network model (CNN Bidirectional GRU CRF Network, CBiGCN), which breaks through the limit of conventional method window, truly realizes end-to-end processing and applies to the neural network model by the five-Tag set method, bias-variable-weight greedy strategy and supplements by Goldstein-Armijo guidelines. Besides, this model, with simple structure, is easy to be operated. And it can automatically learn features, reduces large amounts of tasks on specific knowledge in the form of handcrafted features and data pre-processing, makes use of context information effectively. The authors set an experiment with two data corpuses for Chinese word segmentation to evaluate their system. The experiment verified their new model can obtain better Chinese word segmentation results and greatly reduce training time.

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