用于命名实体识别的可变形堆叠结构
Deformable Stacked Structure for Named Entity Recognition
摘要：
命名实体识别的神经网络结构在自然语言处理领域取得了巨大的成功。目前，主要的架构包括一个双向递归神经网络（RNN）作为编码器和一个条件随机场（CRF）作为解码器。本文提出了一种用于命名实体识别的可变形堆叠结构，其中两个相邻层之间的连接是动态建立的。我们评估的可变形堆叠结构，通过适应不同的层。我们的模型在OntoNotes数据集上实现了最先进的性能。
英文摘要：
Neural architecture for named entity recognition has achieved great success in the field of natural language processing. Currently, the dominating architecture consists of a bi-directional recurrent neural network (RNN) as the encoder and a conditional random field (CRF) as the decoder. In this paper, we propose a deformable stacked structure for named entity recognition, in which the connections between two adjacent layers are dynamically established. We evaluate the deformable stacked structure by adapting it to different layers. Our model achieves the state-of-the-art performances on the OntoNotes dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
