Named Entity Recognition (NER) is a challenging task that extracts named entities from unstructured text data, including news, articles, social comments, etc. The NER system has been studied for decades. Recently, the development of Deep Neural Networks and the progress of pre-trained word embedding have become a driving force for NER. Under such circumstances, how to make full use of the information extracted by word embedding requires more in-depth research. In this paper, we propose an Adversarial Trained LSTM-CNN (ASTRAL) system to improve the current NER method from both the model structure and the training process. In order to make use of the spatial information between adjacent words, Gated-CNN is introduced to fuse the information of adjacent words. Besides, a specific Adversarial training method is proposed to deal with the overfitting problem in NER. We add perturbation to variables in the network during the training process, making the variables more diverse, improving the generalization and robustness of the model. Our model is evaluated on three benchmarks, CoNLL-03, OntoNotes 5.0, and WNUT-17, achieving state-of-the-art results. Ablation study and case study also show that our system can converge faster and is less prone to overfitting.

本文提出了一个名为ASTRAL的Adversarial Trained LSTM-CNN模型，通过引入Gated-CNN和特定的Adversarial training方法，利用word embedding提取的信息来改进当前的Named Entity Recognition（NER）模型结构和训练过程，我们的模型在三个基准测试中，CoNLL-03、OntoNotes 5.0和WNUT-17，都取得了最好的结果。

基于对抗训练的 LSTM-CNN 模型用于命名实体识别