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

Rodrigo Nogueira (), Zhiying Jiang, Kyunghyun Cho and Jimmy Lin
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Rodrigo Nogueira: New York University
Zhiying Jiang: University of Waterloo
Kyunghyun Cho: New York University
Jimmy Lin: University of Waterloo

Scientometrics, 2020, vol. 125, issue 3, No 50, 3016 pages

Abstract: 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.

Keywords: Transformers; Domain adaptation; Citation graph (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11192-020-03718-9

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