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Volume 50 Issue 12
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Journal of Beijing University of Aeronautics and Astronautics  > 2024  >  50(12): 3825-3833.
LI K Y，WANG Y Y，ZHU T Y，et al. Coreference resolution based on graph structure and multitask learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3825-3833 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0941
Citation: LI K Y，WANG Y Y，ZHU T Y，et al. Coreference resolution based on graph structure and multitask learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3825-3833 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0941
shu

Coreference resolution based on graph structure and multitask learning

doi: 10.13700/j.bh.1001-5965.2022.0941

  • Big Data Center of State Grid Corporation of China，Beijing 100031，China

Funds:  Technology Project of Big Data Center of State Grid Corporation of China (SGSJ0000YFJS2200066)
More Information
  • Corresponding author: E-mail：kaiyang2@163.com
  • Received Date: 24 Nov 2022
  • Accepted Date: 29 May 2023
    • Available Online: 10 Jul 2023
  • Publish Date: 06 Jul 2023
    Abstract

  • Coreference resolution is an important task in the domain of natural language processing. Learning effective referential feature representation is a core problem of coreference resolution. It is ineffective to reflect the internal relationships between the information, such as named entities in text fragments and coreference pairs, because the majority of current research views the identification of reference text fragments and the prediction of coreference relationships as two stages of learning. This research proposes a new model of coreference resolution based on graph structure and multitask learning. It combines sequence semantics and structure information to learn referential feature vectors. A multitask learning framework is used to combine the two tasks of coreference resolution and named entity recognition. The two tasks, named entity recognition and coreference resolution, can learn from each other and get better at each other by sharing parameters in the underlying network. Extensive experiments are conducted to verify the superior performance of the proposed model.

     

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