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Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings

Ostendorff, Malte, Rethmeier, Nils, Augenstein, Isabelle, Gipp, Bela, Rehm, Georg·2022·Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
DOI 10.18653/v1/2022.emnlp-main.802
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  • SciNCL is a language model designed to capture the semantic similarity between scientific concepts by leveraging pretrained BERT embeddings.

    “More recently, Ostendorff et al. introduced SciNCL [8], a language model designed to capture the semantic similarity between scientific concepts by leveraging pretrained BERT embeddings.”

    From Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph (2024) · cites this paper as [8]

    The Contributions and Model Training and Implementation sections describe SciNCL as a model for scientific document representations initialized with SciBERT weights. The claim shifts its target from similarity between papers to similarity between scientific concepts, and omits the citation-graph-based sampling central to the method. Page numbers are not available in our copy. (LLM, conf=0.88)

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Metadata

Authors
Ostendorff, M., Rethmeier, N., Augenstein, I., Gipp, B., & Rehm, G.
Venue
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Published
2022
Pages
11670-11688
DOI
10.18653/v1/2022.emnlp-main.802
Cited by
56
References
11

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