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A multi-stage recognizer for nested named entity with weakly labeled data

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Abstract

Existing weakly supervised named entity recognition (NER) research only deals with flat entities and ignores nested entities. This paper proposes a multi-stage nested entity recognition method (MNR) that utilizes weakly labeled data to recognize nested entities. However, weak labels generated through external knowledge bases have two problems: incompleteness and labeling bias. To address this challenge, the MNR comprises two models. First, we propose a neural transition-based attention model (NTAM) to solve the problem of weak-label incompleteness by learning the correlation between words. Simultaneously, the NTAM obtains candidate entities, including nested entities. Second, we propose a multi-marker fusion attention judgment model (MAJM) for selecting candidate entities through context semantics, candidate entities’ meanings, and their boundary information, thereby solving the labeling bias problem. The boundary information of candidate entities is enhanced by fusing their type markers. To our knowledge, we are the first to recognize nested entities under weak supervision by alleviating the noise of weakly labeled data. Experiments on three public nested NER datasets prove the effectiveness of our proposed method under weak supervision and demonstrate that the method outperforms previous state-of-the-art models under supervision.

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The access address of the data used has been given in the article.

Notes

  1. http://www.geniaproject.org/genia-corpus.

  2. https://catalog.ldc.upenn.edu/LDC2005T09. [34]

  3. https://doi.org/10.18170/DVN/SEYRX5.

  4. https://github.com/cambridgeltl/BioNLP-2016.

  5. AILab https://ai.tencent.com/ailab/nlp/en/download.html.

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Acknowledgements

This research work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LGF22F020014), National Key Research and Development Program of China (No. 2020YFB1707700), National Natural Science Foundation of China (No. 62036009, U1909203).

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Nan Gao, Bowei Yang & Peng Chen

  2. School of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Liping Qian

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  1. Nan Gao
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  2. Bowei Yang
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  3. Peng Chen
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Contributions

NG contributed to Conceptualization, Methodology, Writing—review, Visualization. BY contributed to Investigation, Writing—original draft and editing, Software, Validation. PC contributed to Supervision, Project administration, Funding acquisition. LQ contributed to Supervision, Formal analysis.

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Correspondence to Nan Gao.

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Gao, N., Yang, B., Chen, P. et al. A multi-stage recognizer for nested named entity with weakly labeled data. J Supercomput 80, 3663–3693 (2024). https://doi.org/10.1007/s11227-023-05619-z

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