Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96%) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification.
@article{arxiv.1911.10436,
title = {ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition},
author = {Hannah Smith and Zeyu Zhang and John Culnan and Peter Jansen},
journal= {arXiv preprint arXiv:1911.10436},
year = {2019}
}