A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition

2 Jan 2021  ·  Houjin Yu, Xian-Ling Mao, Zewen Chi, Wei Wei, Heyan Huang ·

Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data. Nearly all existing works heavily rely on domain-specific resources, such as external lexicons and knowledge bases. However, such domain-specific resources are often not available, meanwhile it's difficult and expensive to construct the resources, which has become a key obstacle to wider adoption. To tackle the problem, in this work, we propose a novel robust and domain-adaptive approach RDANER for low-resource NER, which only uses cheap and easily obtainable resources. Extensive experiments on three benchmark datasets demonstrate that our approach achieves the best performance when only using cheap and easily obtainable resources, and delivers competitive results against state-of-the-art methods which use difficultly obtainable domainspecific resources. All our code and corpora can be found on https://github.com/houking-can/RDANER.

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Results from the Paper

Ranked #3 on Named Entity Recognition (NER) on SciERC (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Named Entity Recognition (NER) BC5CDR RDANER F1 87.38 # 14
Named Entity Recognition (NER) NCBI-disease RDANER F1 87.89 # 15
Named Entity Recognition (NER) SciERC RDANER F1 68.96 # 3