![]() ![]() 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). The objective of the shared task was to label given research papers with research themes from a total of 36 themes. 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. 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). The review is conducted during the first half of the year.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. Subscribers are given exclusive insights into their views, user profiles and buying behavior in the Asian local currency bond markets. Insurance companies, pension funds and trusts across the Asia are also interviewed. The investor base is comprised of local and regional institutions including asset managers, hedge funds, and local and international banks. These markets are China, CNH, Hong Kong, India, Indonesia, Korea, Malaysia, Philippines, Singapore, Taiwan and Thailand. We survey over 300 fixed income investors across 11 Asian markets. It also provides detailed analysis on the investors' buying behaviour when selecting their counterparties, giving unique access into the minds of these investors. ![]() ![]() It provides a wealth of data on the product needs of investors and the market penetration of the banks that are active in local currency bonds. The Asian Local Currency Bond Benchmark Review has been conducted annually since 2000. ![]()
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