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Navigation-based candidate expansion and pretrained language models for citation recommendation

Author

Listed:
  • Rodrigo Nogueira

    (New York University
    University of Waterloo)

  • Zhiying Jiang

    (University of Waterloo)

  • Kyunghyun Cho

    (New York University
    New York University
    Facebook AI Research
    CIFAR Azrieli Global Scholar)

  • Jimmy Lin

    (University of Waterloo)

Abstract

Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, which we tackle with a two-stage approach: candidate generation followed by reranking. Within this framework, we adapt to the scientific domain a proven combination based on “bag of words” retrieval followed by rescoring with a BERT model. We experimentally show the effects of domain adaptation, both in terms of pretraining on in-domain data and exploiting in-domain vocabulary. In addition, we introduce a novel navigation-based document expansion strategy to enrich the candidate documents fed into our neural models. On three benchmark datasets, our methods achieve or rival the state of the art in the citation recommendation task.

Suggested Citation

  • Rodrigo Nogueira & Zhiying Jiang & Kyunghyun Cho & Jimmy Lin, 2020. "Navigation-based candidate expansion and pretrained language models for citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3001-3016, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03718-9
    DOI: 10.1007/s11192-020-03718-9
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    References listed on IDEAS

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    1. Nicolas Fiorini & Kathi Canese & Grisha Starchenko & Evgeny Kireev & Won Kim & Vadim Miller & Maxim Osipov & Michael Kholodov & Rafis Ismagilov & Sunil Mohan & James Ostell & Zhiyong Lu, 2018. "Best Match: New relevance search for PubMed," PLOS Biology, Public Library of Science, vol. 16(8), pages 1-12, August.
    2. 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.
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    Cited by:

    1. Shicheng Tan & Tao Zhang & Shu Zhao & Yanping Zhang, 2023. "Self-supervised scientific document recommendation based on contrastive learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5027-5049, September.
    2. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
    3. 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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