论文标题
星体：对对抗性训练的LSTM-CNN命名实体识别
ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition
论文作者
论文摘要
命名实体识别（NER）是一项具有挑战性的任务，从非结构化的文本数据中提取命名实体，包括新闻，文章，社交评论等。数十年来已经研究了NER系统。最近，深度神经网络的发展和预训练的单词嵌入的进步已成为NER的驱动力。在这种情况下，如何充分利用单词嵌入提取的信息需要更多的深入研究。在本文中，我们提出了一个受对抗性训练的LSTM-CNN（星体）系统，以从模型结构和训练过程中改善当前的NER方法。为了利用相邻单词之间的空间信息，介绍了门控CNN以融合相邻单词的信息。此外，提出了一种特定的对抗训练方法来处理NER中的过度拟合问题。我们在训练过程中为网络中的变量增加了扰动，从而使变量更加多样化，从而提高了模型的概括和鲁棒性。我们的模型在三个基准测试基准（Conll-03，Ontonotes 5.0和Wnut-17）上进行了评估，从而实现了最先进的结果。消融研究和案例研究还表明，我们的系统可以更快地收敛，并且不容易过度拟合。
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.
