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Multi-task learning model for citation intent classification in scientific publications

Author

Listed:
  • Ruihua Qi

    (Dalian University of Foreign Languages
    Dalian University of Foreign Languages)

  • Jia Wei

    (Dalian University of Foreign Languages)

  • Zhen Shao

    (Dalian University of Foreign Languages)

  • Zhengguang Li

    (Dalian University of Foreign Languages)

  • Heng Chen

    (Dalian University of Foreign Languages)

  • Yunhao Sun

    (Dalian University of Foreign Languages)

  • Shaohua Li

    (Dalian University of Foreign Languages)

Abstract

Citations play a significant role in the evaluation of scientific literature and researchers. Citation intent analysis is essential for academic literature understanding. Meanwhile, it is useful for enriching semantic information representation for the citation intent classification task because of the rapid growth of publicly accessible full-text literature. However, some useful information that is readily available in citation context and facilitates citation intent analysis has not been fully explored. Furthermore, some deep learning models may not be able to learn relevant features effectively due to insufficient training samples of citation intent analysis tasks. Multi-task learning aims to exploit useful information between multiple tasks to help improve learning performance and exhibits promising results on many natural language processing tasks. In this paper, we propose a joint semantic representation model, which consists of pretrained language models and heterogeneous features of citation intent texts. Considering the correlation between citation intents, citation section and citation worthiness classification tasks, we build a multi-task citation classification framework with soft parameter sharing constraint and construct independent models for multiple tasks to improve the performance of citation intent classification. The experimental results demonstrate that the heterogeneous features and the multi-task framework with soft parameter sharing constraint proposed in this paper enhance the overall citation intent classification performance.

Suggested Citation

  • Ruihua Qi & Jia Wei & Zhen Shao & Zhengguang Li & Heng Chen & Yunhao Sun & Shaohua Li, 2023. "Multi-task learning model for citation intent classification in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(12), pages 6335-6355, December.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:12:d:10.1007_s11192-023-04858-4
    DOI: 10.1007/s11192-023-04858-4
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    References listed on IDEAS

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    1. Xiaodan Zhu & Peter Turney & Daniel Lemire & André Vellino, 2015. "Measuring academic influence: Not all citations are equal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 408-427, February.
    2. Yang Zhang & Rongying Zhao & Yufei Wang & Haihua Chen & Adnan Mahmood & Munazza Zaib & Wei Emma Zhang & Quan Z. Sheng, 2022. "Correction to: Towards employing native information in citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6579-6579, November.
    3. Faiza Qayyum & Muhammad Tanvir Afzal, 2019. "Identification of important citations by exploiting research articles’ metadata and cue-terms from content," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 21-43, January.
    4. Saeed-Ul Hassan & Mubashir Imran & Sehrish Iqbal & Naif Radi Aljohani & Raheel Nawaz, 2018. "Deep context of citations using machine-learning models in scholarly full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1645-1662, December.
    5. Yang Zhang & Rongying Zhao & Yufei Wang & Haihua Chen & Adnan Mahmood & Munazza Zaib & Wei Emma Zhang & Quan Z. Sheng, 2022. "Towards employing native information in citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6557-6577, November.
    6. Xiaorui Jiang & Jingqiang Chen, 2023. "Contextualised segment-wise citation function classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5117-5158, September.
    7. Dongqing Lyu & Xuanmin Ruan & Juan Xie & Ying Cheng, 2021. "The classification of citing motivations: a meta-synthesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3243-3264, April.
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    Cited by:

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    2. Xiaorui Jiang, 2025. "Ensembling approaches to citation function classification and important citation screening," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(3), pages 1371-1419, March.

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