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Fine-grained Contrastive Learning for Relation Extraction.

, , and . EMNLP, page 1083-1095. Association for Computational Linguistics, (2022)

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Representing the Aboutness of a Diagnosis.. MIE, volume 247 of Studies in Health Technology and Informatics, page 805-809. IOS Press, (2018)Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting., , and . EMNLP, page 14227-14242. Association for Computational Linguistics, (2023)READ: Improving Relation Extraction from an ADversarial Perspective., , and . NAACL-HLT (Findings), page 533-548. Association for Computational Linguistics, (2024)An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods.. CoRR, (2022)Machine learning and natural language processing for classifying infant feeding status from clinical notes., , , , , , , , , and 2 other author(s). AMIA, AMIA, (2022)Fine-grained Contrastive Learning for Relation Extraction., , and . EMNLP, page 1083-1095. Association for Computational Linguistics, (2022)BFO 2.0 Specification and User Guide, , , , , , , , , and 1 other author(s). (2015)An Efficient Concurrent Auction Model for Firm Natural Gas Transportation Capacity. INFOR, 30 (3): 240-256 (1992)Computation of Convex Hull Prices in Electricity Markets with Non-Convexities using Dantzig-Wolfe Decomposition., , and . CoRR, (2020)A Semantic Web Representation of Entire Populations., , , and . ICBO/BioCreative, volume 1747 of CEUR Workshop Proceedings, CEUR-WS.org, (2016)