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Linguistic Dependency Guided Graph Convolutional Networks for Named Entity Recognition

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13088))

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Abstract

The GCN model used for named entity recognition (NER) tasks reflects promising results by capturing the long-distance syntactic dependency between words in sentences. However, existing models focus on the syntactic relations, we study the usefulness of linguistic, including semantic and syntactic dependency types information for NER. Through experiments on the OntoNotes 5.0 data set and ConLL2003 data set, we have demonstrated the significant improvement of our new SDP-GCN NER model.

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Notes

  1. 1.

    A Python package that includes many state-of-the-art syntactic/semantic parsers, https://github.com/yzhangcs/parser/.

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Author information

Authors and Affiliations

  1. 35th Floor, Maoye Building, No. 78 Haihe East Road, Guangfu Road Street, Hebei District, Tianjin, China

    Ximin Sun, Jing Zhou, Shuai Wang, Xiaoming Li, Bin Zheng & Dan Liu

Authors
  1. Ximin Sun
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  2. Jing Zhou
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  3. Shuai Wang
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  4. Xiaoming Li
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  5. Bin Zheng
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  6. Dan Liu
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Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

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Sun, X., Zhou, J., Wang, S., Li, X., Zheng, B., Liu, D. (2022). Linguistic Dependency Guided Graph Convolutional Networks for Named Entity Recognition. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_18

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  • DOI: https://doi.org/10.1007/978-3-030-95408-6_18

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  • Online ISBN: 978-3-030-95408-6

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