Enhancing the examination of obstacles in an automated peer review system

Author:

Fernandes Gustavo LúciusORCID,Vaz-de-Melo Pedro O. S.ORCID

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences

Reference48 articles.

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2. Anjum, O., Gong, H., Bhat, S., et al.: PaRe: A paper-reviewer matching approach using a common topic space. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 518–528, (2019) https://doi.org/10.18653/v1/D19-1049, https://aclanthology.org/D19-1049

3. Bartoli, A., De Lorenzo, A., Medvet, E., et al.: Your paper has been accepted, rejected, or whatever: Automatic generation of scientific paper reviews. In: Buccafurri, F., Holzinger, A., Kieseberg, P., et al. (eds.) Availability, Reliability, and Security in Information Systems, pp. 19–28. Springer International Publishing, Cham (2016)

4. Beigman Klebanov, B., Beigman, E.: Squibs: From annotator agreement to noise models. Computational Linguistics 35(4), 495–503 (2009) https://doi.org/10.1162/coli.2009.35.4.35402, URL https://aclanthology.org/J09-4005

5. Beigman Klebanov., B., Beigman, E.: Difficult cases: From data to learning, and back. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, pp 390–396, (2014) https://doi.org/10.3115/v1/P14-2064, https://aclanthology.org/P14-2064

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1. Editorial to the special issue on JCDL 2022;International Journal on Digital Libraries;2024-06

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