Application of Pre-training Models in Named Entity Recognition

• URL: http://arxiv.org/abs/2002.08902v1
• Date: Sun, 9 Feb 2020 08:18:20 GMT
• Title: Application of Pre-training Models in Named Entity Recognition
• Authors: Yu Wang, Yining Sun, Zuchang Ma, Lisheng Gao, Yang Xu, Ting Sun
• Abstract summary: We introduce the architecture and pre-training tasks of four common pre-training models: BERT, ERNIE, ERNIE2.0-tiny, and RoBERTa. We apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task. Experiment results showed that RoBERTa achieved state-of-the-art results on the MSRA-2006 dataset.
• Score: 5.285449619478964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task to extract entities from unstructured data. The previous methods for NER were based on machine learning or deep learning. Recently, pre-training models have significantly improved performance on multiple NLP tasks. In this paper, firstly, we introduce the architecture and pre-training tasks of four common pre-training models: BERT, ERNIE, ERNIE2.0-tiny, and RoBERTa. Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task. The experiment results showed that RoBERTa achieved state-of-the-art results on the MSRA-2006 dataset.

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