  The task of definition detection is important for scholarly papers, because
papers often make use of technical terminology that may be unfamiliar to
readers. Despite prior work on definition detection, current approaches are far
from being accurate enough to use in real-world applications. In this paper, we
first perform in-depth error analysis of the current best performing definition
detection system and discover major causes of errors. Based on this analysis,
we develop a new definition detection system, HEDDEx, that utilizes syntactic
features, transformer encoders, and heuristic filters, and evaluate it on a
standard sentence-level benchmark. Because current benchmarks evaluate randomly
sampled sentences, we propose an alternative evaluation that assesses every
sentence within a document. This allows for evaluating recall in addition to
precision. HEDDEx outperforms the leading system on both the sentence-level and
the document-level tasks, by 12.7 F1 points and 14.4 F1 points, respectively.
We note that performance on the high-recall document-level task is much lower
than in the standard evaluation approach, due to the necessity of incorporation
of document structure as features. We discuss remaining challenges in
document-level definition detection, ideas for improvements, and potential
issues for the development of reading aid applications.
