  Domain-specific named entity recognition (NER) on Computer Science (CS)
scholarly articles is an information extraction task that is arguably more
challenging for the various annotation aims that can beset the task and has
been less studied than NER in the general domain. Given that significant
progress has been made on NER, we believe that scholarly domain-specific NER
will receive increasing attention in the years to come. Currently, progress on
CS NER -- the focus of this work -- is hampered in part by its recency and the
lack of a standardized annotation aim for scientific entities/terms. This work
proposes a standardized task by defining a set of seven contribution-centric
scholarly entities for CS NER viz., research problem, solution, resource,
language, tool, method, and dataset. Following which, its main contributions
are: combines existing CS NER resources that maintain their annotation focus on
the set or subset of contribution-centric scholarly entities we consider;
further, noting the need for big data to train neural NER models, this work
additionally supplies thousands of contribution-centric entity annotations from
article titles and abstracts, thus releasing a cumulative large novel resource
for CS NER; and, finally, trains a sequence labeling CS NER model inspired
after state-of-the-art neural architectures from the general domain NER task.
Throughout the work, several practical considerations are made which can be
useful to information technology designers of the digital libraries.
