基于神经网络与注意力机制的中文文本校对方法

中文文本校对是中文自然语言处理方面的关键任务之一，人工校对方式难以满足日常工作的数据量需求，而基于统计的文本校对方法不能灵活的处理语义方面的错误.针对上述问题，提出了一种基于神经网络与注意力机制的中文文本校对方法.利用双向门控循环神经网络层获取文本信息并进行特征提取，并引入注意力机制层增强词间语义逻辑关系的捕获能力.在基于Keras深度学习框架下对模型进行实现，实验结果表明，该方法能够对含语义错误的文本进行校对.

Chinese Text Proofreading Method Based on Neural Network and Attention Mechanism

Chinese text proofreading is one of the key tasks in Chinese natural language processing, and manual proofreading is difficult to meet the data volume requirement of daily work, and the text proofreading method based on statistics can not deal with semantic errors flexibly. Aiming at the above problems, a Chinese text proofreading method based on neural network and attention mechanism is proposed. The bidirectional Gated Recurrent Unity neural network layer is used to obtain text information and feature extraction, and the ability of attention mechanism layer to enhance the semantic logic relation between words is introduced. The model is implemented under the framework of deep learning based on Keras. Experimental results show that this method can proofread text with semantic errors.