摘要
命名实体识别是自然语言处理中一项重要的基础任务,本文提出一种简单、新颖的深层循环神经网络的命名实体识别(Named Entity Recognition,NER)方法。使用一种稠密连接的方式(Dense connection,DC)在多层的双向长短期记忆神经网络(bi-directional long short-term memory, Bi-LSTM)之间传递信息,称这种网络结构为DC-BiLSTM。利用DC-BiLSTM来学习句子特征,并采用自注意力机制(self-attention)来捕捉任意两个标记之间的关系,最后使用条件随机场(CRF)对整个句子进行解码预测。实验表明结果,该方法在MSRA语料上平均F1值能达到91.81%,最高F1值能达到92.05%。
Named entity recognition(NER) is an important basic task in natural language processing(NLP). In this paper, a simple and novel neural network model for NER is proposed. And a dense connection model(DC) is introduced to transfer information between multiple layers of bi-directional long short-term memories(Bi-LSTM), which are named DC-BiLSTM. DC-BiLSTM is applied to automatically learn sentence features and self-attention mechanism is used to capture the relationships between two tokens. At last, Conditional Random Field(CRF) is adopted for sentence level prediction. The results show that this method achieves F1 92.05% and average F1 91.81% in MSRA without any artificial feature.
作者
刘晓俊
辜丽川
史先章
LIU Xiaojun;GU Lichuan;SHI Xianzhang(Anhui Agricultural University,Hefei 230036,China)
出处
《洛阳理工学院学报（自然科学版）》
2019年第1期65-70,77,共7页
Journal of Luoyang Institute of Science and Technology：Natural Science Edition
基金
国家自然科学基金项目(31771679)
