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Ranking Facts for Explaining Answers to Elementary Science Questions ...
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SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph ...
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Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles ...
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Fine-tuning BERT with Focus Words for Explanation Regeneration ...
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The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ...
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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 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; Digital Libraries cs.DL; FOS Computer and information sciences; Information Retrieval cs.IR
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URL: https://arxiv.org/abs/2003.01006 https://dx.doi.org/10.48550/arxiv.2003.01006
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The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources ...
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Domain-Independent Extraction of Scientific Concepts from Research Articles ...
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Domain-independent Extraction of Scientific Concepts from Research Articles ...
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