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Citation recommendation based on argumentative zoning of user queries

Author

Listed:
  • Ma, Shutian
  • Zhang, Chengzhi
  • Zhang, Heng
  • Gao, Zheng
Abstract
Citation recommendation aims to locate the important papers for scholars to cite. When writing the citing sentences, the authors usually hold different citing intents, which are referred to citation function in citation analysis. Since argumentative zoning is to identify the argumentative and rhetorical structure in scientific literature, we want to use this information to improve the citation recommendation task. In this paper, a multi-task learning model is built for citation recommendation and argumentative zoning classification. We also generated an annotated corpus of the data from PubMed Central based on a new argumentative zoning schema. The experimental results show that, by considering the argumentative information in the citing sentence, citation recommendation model will get better performance.

Suggested Citation

  • Ma, Shutian & Zhang, Chengzhi & Zhang, Heng & Gao, Zheng, 2025. "Citation recommendation based on argumentative zoning of user queries," Journal of Informetrics, Elsevier, vol. 19(1).
    • Handle: RePEc:eee:infome:v:19:y:2025:i:1:s1751157724001196
    DOI: 10.1016/j.joi.2024.101607
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    References listed on IDEAS

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    1. Alhoori, Hamed & Furuta, Richard, 2017. "Recommendation of scholarly venues based on dynamic user interests," Journal of Informetrics, Elsevier, vol. 11(2), pages 553-563.
    2. Zhang, Chengzhi & Liu, Lifan & Wang, Yuzhuo, 2021. "Characterizing references from different disciplines: A perspective of citation content analysis," Journal of Informetrics, Elsevier, vol. 15(2).
    3. Shazia Usmani & Jawwad A Shamsi, 2023. "LSTM based stock prediction using weighted and categorized financial news," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-27, March.
    4. Indra Budi & Yaniasih Yaniasih, 2023. "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 735-759, January.
    5. Shutian Ma & Chengzhi Zhang & Xiaozhong Liu, 2020. "A review of citation recommendation: from textual content to enriched context," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1445-1472, March.
    6. Chanwoo Jeong & Sion Jang & Eunjeong Park & Sungchul Choi, 2020. "A context-aware citation recommendation model with BERT and graph convolutional networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1907-1922, September.
    7. Hei-Chia Wang & Jen-Wei Cheng & Che-Tsung Yang, 2022. "SentCite: a sentence-level citation recommender based on the salient similarity among multiple segments," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2521-2546, May.
    8. Zafar Ali & Irfan Ullah & Amin Ul Haq & Asim Ullah Jan & Khan Muhammad, 2021. "Correction to: An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8771-8771, October.
    9. Esra Gündoğan & Mehmet Kaya & Ali Daud, 2023. "Deep learning for journal recommendation system of research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 461-481, January.
    10. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    11. Kim, Ha Jin & Jeong, Yoo Kyung & Song, Min, 2016. "Content- and proximity-based author co-citation analysis using citation sentences," Journal of Informetrics, Elsevier, vol. 10(4), pages 954-966.
    12. Chanathip Pornprasit & Xin Liu & Pattararat Kiattipadungkul & Natthawut Kertkeidkachorn & Kyoung-Sook Kim & Thanapon Noraset & Saeed-Ul Hassan & Suppawong Tuarob, 2022. "Enhancing citation recommendation using citation network embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 233-264, January.
    13. 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.
    14. Zafar Ali & Irfan Ullah & Amin Khan & Asim Ullah Jan & Khan Muhammad, 2021. "An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4083-4119, May.
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    Cited by:

    1. Krittin Chatrinan & Thanapon Noraset & Suppawong Tuarob, 2025. "GAN-CITE: leveraging semi-supervised generative adversarial networks for citation function classification with limited data," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(2), pages 679-703, February.

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