基于膨胀卷积神经网络模型的中文分词方法

目前,许多深度神经网络模型以双向长短时记忆网络结构处理中文分词任务,存在输入特征不够丰富、语义理解不全、计算速度慢的问题。针对以上问题,该文提出一种基于膨胀卷积神经网络模型的中文分词方法。通过加入汉字字根信息并用卷积神经网络提取特征来丰富输入特征;使用膨胀卷积神经网络模型并加入残差结构进行训练,能够更好理解语义信息并提高计算速度。基于Bakeoff 2005语料库的4个数据集设计实验,与双向长短时记忆网络模型的中文分词方法做对比,实验表明该文提出的模型取得了更好的分词效果,并具有更快的计算速度。

Dilated Convolution Neural Networks for Chinese Word Segmentation

At present, many deep neural network models deal with Chinese word segmentation tasks with bidirectional long short term memory neural network structure. Issues remain in the aspects that the input features are not rich enough, the semantic understanding is not complete, and the calculation speed is slow. In this paper, a dilated convolution neural networks for Chinese Word Segmentation is proposed. Chinese radical information is integrated to enrich the input and the convolution neural network is applied to extract the feature. The dilated convolution neural networks with residual structure can better understand semantic information and improve training efficiency. Experimented on the four datasets in Bakeoff 2005, the proposed method achieves better performance in terms of accuracy and efficiency compared with the bidirectional long short term memory neural network method.