  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.
