The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources

• DOI: 10.34657/5190
• Publication Date: 2020-01-01
• Authors: D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur; Jaradeh, Mohamad ...; Auer, Sören; Ewerth, Ralph
• Keywords: FOS: Computer and information sciences; 020; Computer Science - Computation and Language; Computer Science - Artificial Intelligence; Language Resource; Entity Classification; Computer Science - Digital Libraries; Entity Resolution; Entity Linking; 01 natural sciences; Computer Science - Information Retrieval; Entity Recognition; Artificial Intelligence (cs.AI); Evaluation Corpus; Digital Libraries (cs.DL); Word Sense Disambiguation; Computation and Language (cs.CL); Konferenzschrift; Information Retrieval (cs.IR); 0105 earth and related environmental sciences

Published in LREC 2020. Publication URL https://www.aclweb.org/anthology/2020.lrec-1.268/; Dataset DOI https://doi.org/10.25835/0017546<br/>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 for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.<br/>