![]() ![]() The objective of the shared task was to label given research papers with research themes from a total of 36 themes. We present a new gold-standard dataset and a benchmark for the Research Theme Identification task, a sub-task of the Scholarly Knowledge Graph Generation shared task, at the 3rd Workshop on Scholarly Document Processing. Proceedings of the Third Workshop on Scholarly Document ProcessingĪssociation for Computational Linguistics Both data and the ensemble are publicly available on label.csv and, respectively.",īenchmark for Research Theme Classification of Scholarly Documents It uses a weighted sum aggregation for the multiple predictions to obtain a final single prediction for the given research paper. The ensemble involves enriching the initial data with additional information from open-access digital libraries and Argumentative Zoning techniques (CITATION). title, abstract, reference), with traditional machine learning models. ![]() We provide a performance comparison of a transformer-based ensemble, which obtains multiple predictions for a research paper, given its multiple textual fields (e.g. The benchmark was compiled using data drawn from the largest overall assessment of university research output ever undertaken globally (the Research Excellence Framework - 2014). Publisher = "Association for Computational Linguistics",Ībstract = "We present a new gold-standard dataset and a benchmark for the Research Theme Identification task, a sub-task of the Scholarly Knowledge Graph Generation shared task, at the 3rd Workshop on Scholarly Document Processing. Mendoza et al., sdp 2022) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Code = "Benchmark for Research Theme Classification of Scholarly Documents",īooktitle = "Proceedings of the Third Workshop on Scholarly Document Processing", Cite (Informal): Benchmark for Research Theme Classification of Scholarly Documents (E. Association for Computational Linguistics. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 253–262, Gyeongju, Republic of Korea. Benchmark for Research Theme Classification of Scholarly Documents. Mendoza, Wojciech Kusa, Alaa El-Ebshihy, Ronin Wu, David Pride, Petr Knoth, Drahomira Herrmannova, Florina Piroi, Gabriella Pasi, and Allan Hanbury. Anthology ID: 2022.sdp-1.31 Volume: Proceedings of the Third Workshop on Scholarly Document Processing Month: October Year: 2022 Address: Gyeongju, Republic of Korea Venue: sdp SIG: Publisher: Association for Computational Linguistics Note: Pages: 253–262 Language: URL: DOI: Bibkey: e-mendoza-etal-2022-benchmark Cite (ACL): Óscar E. Both data and the ensemble are publicly available on and, respectively. ![]() ![]() Abstract We present a new gold-standard dataset and a benchmark for the Research Theme Identification task, a sub-task of the Scholarly Knowledge Graph Generation shared task, at the 3rd Workshop on Scholarly Document Processing. ![]()
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