ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition
This addresses the problem of overfitting and limited spatial information use in NER for applications like text analysis, though it appears incremental as it builds on existing deep learning methods.
The paper tackles Named Entity Recognition by proposing ASTRAL, an adversarial trained LSTM-CNN system that improves model generalization and robustness, achieving state-of-the-art results on benchmarks like CoNLL-03, OntoNotes 5.0, and WNUT-17.
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.