Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation

• URL: http://arxiv.org/abs/2407.18562v1
• Date: Fri, 26 Jul 2024 07:30:41 GMT
• Title: Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation
• Authors: Chaoyi Ai, Yong Jiang, Shen Huang, Pengjun Xie, Kewei Tu,
• Abstract summary: Named entity recognition (NER) models often struggle with noisy inputs. We propose a more realistic setting in which only noisy text and its NER labels are available. We employ a multi-view training framework that improves robust NER without retrieving text during inference.
• Score: 67.89838237013078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named entity recognition (NER) models often struggle with noisy inputs, such as those with spelling mistakes or errors generated by Optical Character Recognition processes, and learning a robust NER model is challenging. Existing robust NER models utilize both noisy text and its corresponding gold text for training, which is infeasible in many real-world applications in which gold text is not available. In this paper, we consider a more realistic setting in which only noisy text and its NER labels are available. We propose to retrieve relevant text of the noisy text from a knowledge corpus and use it to enhance the representation of the original noisy input. We design three retrieval methods: sparse retrieval based on lexicon similarity, dense retrieval based on semantic similarity, and self-retrieval based on task-specific text. After retrieving relevant text, we concatenate the retrieved text with the original noisy text and encode them with a transformer network, utilizing self-attention to enhance the contextual token representations of the noisy text using the retrieved text. We further employ a multi-view training framework that improves robust NER without retrieving text during inference. Experiments show that our retrieval-augmented model achieves significant improvements in various noisy NER settings.

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