The dominant approaches for named entity recognition (NER) mostly adopt
complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM).
However, RNNs are limited by their recurrent nature in terms of computational
efficiency. In contrast, convolutional neural networks (CNN) can fully exploit
the GPU parallelism with their feedforward architectures. However, little
attention has been paid to performing NER with CNNs, mainly owing to their
difficulties in capturing the long-term context information in a sequence. In
this paper, we propose a simple but effective CNN-based network for NER, i.e.,
gated relation network (GRN), which is more capable than common CNNs in
capturing long-term context. Specifically, in GRN we firstly employ CNNs to
explore the local context features of each word. Then we model the relations
between words and use them as gates to fuse local context features into global
ones for predicting labels. Without using recurrent layers that process a
sentence in a sequential manner, our GRN allows computations to be performed in
parallel across the entire sentence. Experiments on two benchmark NER datasets
(i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve
state-of-the-art performance with or without external knowledge. It also enjoys
lower time costs to train and test.We have made the code publicly available at
this https URL

本文提出了一种基于 gated relation network 的卷积神经网络模型，用于更好地捕捉词汇间的长期关联以用于命名实体识别，实验结果表明，该模型可以在 CoNLL2003 和 Ontonotes 5.0 数据集上实现最先进的性能。