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• W2963670884 abstract "This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task." @default.
• W2963670884 created "2019-07-30" @default.
• W2963670884 creator A5074697516 @default.
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• W2963670884 date "2017-01-01" @default.
• W2963670884 modified "2023-09-25" @default.
• W2963670884 title "Scientific Information Extraction with Semi-supervised Neural Tagging" @default.
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• W2963670884 doi "https://doi.org/10.18653/v1/d17-1279" @default.
• W2963670884 hasPublicationYear "2017" @default.
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