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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 toprovide a benchmark for the evaluation of scientific entity extraction,classification, and resolution tasks in a domain-independent fashion. Itcomprises abstracts in 10 STEM disciplines that were found to be the mostprolific ones on a major publishing platform. We describe the creation of sucha multidisciplinary corpus and highlight the obtained findings in terms of thefollowing features: 1) a generic conceptual formalism for scientific entitiesin a multidisciplinary scientific context; 2) the feasibility of thedomain-independent human annotation of scientific entities under such a genericformalism; 3) a performance benchmark obtainable for automatic extraction ofmultidisciplinary scientific entities using BERT-based neural models; 4) adelineated 3-step entity resolution procedure for human annotation of thescientific entities via encyclopedic entity linking and lexicographic wordsense disambiguation; and 5) human evaluations of Babelfy returned encyclopediclinks and lexicographic senses for our entities. Our findings cumulativelyindicate that human annotation and automatic learning of multidisciplinaryscientific concepts as well as their semantic disambiguation in a wide-rangingsetting as STEM is reasonable.

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