关键词: 自然语言处理, 中文分词, 卷积神经网络, 字向量

Abstract: In Chinese information processing, word segmentation is a very common and critical task. Usually, the first step of the Chinese Natural Language Processing（NLP） tasks is word segmentation. Over the years, the method of Chinese word segmentation has evolved from machine learning to deep learning. However, most of the models have various deficiencies such as the models being too complex, relying heavily on hand-crafted features, and having poor performance on Out of Vocabulary（OOV） words. This paper proposes a PCNN （Pure CNN） Chinese word segmentation model based on Convolutional Neural Networks（CNN）. This model uses the word vector context window to label the words. It has a simple structure and does not rely on the hand-crafted features, good stability, high accuracy and other advantages. Considering the characteristics of the distributed word vector itself, there is no need for pooling in the PCNN model. The features data extracted from the convolution layer are preserved, and the training speed of the model is greatly improved. The experimental results on public datasets show that the accuracy of the model is reached other neural network models. At the same time, it is also verified in the comparison experiment that the network model without pooling layer is superior to the network model with pooling layer.

Key words: Natural Language Processing（NLP）, Chinese word segmentation, Convolutional Neural Networks（CNN）, word vector