用于社交媒体的中文命名实体识别

社交领域的中文命名实体识别(NER)是自然语言处理(NLP)中一项重要的基础任务。目前基于词粒度信息或者外部知识的中文命名实体识别方法,都会受到中文分词(CWS)和溢出词(OOV)等问题的影响。因此,该文提出了一种基于字符的使用位置编码和多种注意力的对抗学习模型。联合使用位置编码和多头注意力能够更好地捕获字序间的依赖关系,而使用空间注意力的判别器则能改善对外部知识的提取效果。该文模型分别在Weibo2015 数据集和Weibo2017数据集上进行了实验,实验结果中的F₁值分别为56.79%和60.62%。与多个基线模型相比,该文提出的模型性能更优。

Chinese Named Entity Recognition for Social Media

Chinese named entity recognition (NER) in social media is a challenging task. Existing methods based on word-level information or external knowledge are affected by Chinese word segmentation (CWS) and Out-of-Vocabulary (OOV). This paper proposes an adversarial learning model based on character using positional encoding and multi-attention. The combination of positional encoding and self-attention can better capture the dependence of character sequences, while the use of spatial attention discriminator can improve the extraction effect of external knowledge. The experimental results show that the proposed approach achieves 56.79% and 60.62% in F-score, respectively, on the datasets in Weibo2015 and Weibo2017.