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        <title>Rethinking Negative Sampling for Handling Missing Entity Annotations</title>
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        <namePart type="given">Yangming</namePart>
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    <abstract>Negative sampling is highly effective in handling missing annotations for named entity recognition (NER). One of our contributions is an analysis on how it makes sense through introducing two insightful concepts: missampling and uncertainty. Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling. Based on the sparsity of named entities, we also theoretically derive a lower bound for the probability of zero missampling rate, which is only relevant to sentence length. The other contribution is an adaptive and weighted sampling distribution that further improves negative sampling via our former analysis. Experiments on synthetic datasets and well-annotated datasets (e.g., CoNLL-2003) show that our proposed approach benefits negative sampling in terms of F1 score and loss convergence. Besides, models with improved negative sampling have achieved new state-of-the-art results on real-world datasets (e.g., EC).</abstract>
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%0 Conference Proceedings
%T Rethinking Negative Sampling for Handling Missing Entity Annotations
%A Li, Yangming
%A Liu, Lemao
%A Shi, Shuming
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F li-etal-2022-rethinking
%X Negative sampling is highly effective in handling missing annotations for named entity recognition (NER). One of our contributions is an analysis on how it makes sense through introducing two insightful concepts: missampling and uncertainty. Empirical studies show low missampling rate and high uncertainty are both essential for achieving promising performances with negative sampling. Based on the sparsity of named entities, we also theoretically derive a lower bound for the probability of zero missampling rate, which is only relevant to sentence length. The other contribution is an adaptive and weighted sampling distribution that further improves negative sampling via our former analysis. Experiments on synthetic datasets and well-annotated datasets (e.g., CoNLL-2003) show that our proposed approach benefits negative sampling in terms of F1 score and loss convergence. Besides, models with improved negative sampling have achieved new state-of-the-art results on real-world datasets (e.g., EC).
%R 10.18653/v1/2022.acl-long.497
%U https://aclanthology.org/2022.acl-long.497
%U https://doi.org/10.18653/v1/2022.acl-long.497
%P 7188-7197

Markdown (Informal)

[Rethinking Negative Sampling for Handling Missing Entity Annotations](https://aclanthology.org/2022.acl-long.497) (Li et al., ACL 2022)

• Rethinking Negative Sampling for Handling Missing Entity Annotations (Li et al., ACL 2022)

ACL

• Yangming Li, Lemao Liu, and Shuming Shi. 2022. Rethinking Negative Sampling for Handling Missing Entity Annotations. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7188–7197, Dublin, Ireland. Association for Computational Linguistics.