Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Authors

  • Wenkai Zhang Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences University of Chinese Academy of Sciences
  • Hongyu Lin Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences
  • Xianpei Han Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences
  • Le Sun Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences
  • Huidan Liu Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences
  • Zhicheng Wei Huawei Cloud&AI
  • Nicholas Yuan Huawei Cloud&AI

Keywords:

Information Extraction

Abstract

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.

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Published

2021-05-18

How to Cite

Zhang, W., Lin, H., Han, X., Sun, L., Liu, H., Wei, Z., & Yuan, N. (2021). Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14481-14488. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17702

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III