基于注意力长短时记忆网络的中文词性标注模型

针对传统的基于统计模型的词性标注存在人工特征依赖的问题,提出一种有效的基于注意力长短时记忆网络的中文词性标注模型。该模型以基本的分布式词向量作为单元输入,利用双向长短时记忆网络提取丰富的词语上下文特征表示。同时在网络中加入注意力隐层,利用注意力机制为不同时刻的隐状态分配概率权重,使隐层更加关注重要特征,从而优化和提升隐层向量的质量。在解码过程中引入状态转移概率矩阵,以进一步提升标注准确率。在《人民日报》和中文宾州树库CTB5语料上的实验结果表明,该模型能够有效地进行中文词性标注,其准确率高于条件随机场等传统词性标注方法,与当前较好的词性标注模型也十分接近。

Chinese Part-of-speech Tagging Model Using Attention-based LSTM

Because traditional statistical model based Chinese part-of-speech tagging relies heavily on manually designed features,this paper proposed an effective attention based long short-term memory model for Chinese part-of-speech tagging.The proposed model utilizes the basic distributed word vector as the unit input,and extracts rich contextual feature representation with bidirectional long short-term memory.At the same time,an attention based hidden layer is added in the network,and the attention probability is distributed for hidden state in different time to optimize and improve the quality of hidden vector.The state transition probability is employed in decoding process to further improve accuracy.Experimental results on PKU and CTB5 dataset show that the proposed model is able to make Chinese part-of-speech tagging effectively.It achieves higher accuracy than traditional methods and gets competitive results compared with state-of-the-art models.