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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Ranking Facts for Explaining Answers to Elementary Science Questions ... D'Souza, Jennifer; Mulang', Isaiah Onando; Auer, Soeren. - : arXiv, 2021 BASE Show details 2 SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Auer, Sören; D'Souza, Jennifer. - : Underline Science Inc., 2021 BASE Show details 3 Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles ... D'Souza, Jennifer; Auer, Soeren. - : arXiv, 2021 BASE Show details 4 Fine-tuning BERT with Focus Words for Explanation Regeneration ... The 28th International Conference on Computational Linguistics 2020; Auer, Sören; D'Souza, Jennifer. - : Underline Science Inc., 2020 BASE Show details 5 The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ... D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur; Jaradeh, Mohamad Yaser; Auer, Sören; Ewerth, Ralph. - : arXiv, 2020 Abstract: We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure ... : Published in LREC 2020. Publication URL https://www.aclweb.org/anthology/2020.lrec-1.268/; Dataset DOI https://doi.org/10.25835/0017546 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; Digital Libraries cs.DL; FOS Computer and information sciences; Information Retrieval cs.IR URL: https://arxiv.org/abs/2003.01006 https://dx.doi.org/10.48550/arxiv.2003.01006 BASE Hide details 6 The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ... D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur. - : Paris : European Language Resources Association, 2020 BASE Show details 7 Domain-Independent Extraction of Scientific Concepts from Research Articles ... Brack, Arthur; D'Souza, Jennifer; Hoppe, Anett. - : Cham : Springer, 2020 BASE Show details 8 Domain-independent Extraction of Scientific Concepts from Research Articles ... Brack, Arthur; D'Souza, Jennifer; Hoppe, Anett. - : arXiv, 2020 BASE Show details

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