  Identifying and extracting data elements such as study descriptors in
publication full texts is a critical yet manual and labor-intensive step
required in a number of tasks. In this paper we address the question of
identifying data elements in an unsupervised manner. Specifically, provided a
set of criteria describing specific study parameters, such as species, route of
administration, and dosing regimen, we develop an unsupervised approach to
identify text segments (sentences) relevant to the criteria. A binary
classifier trained to identify publications that met the criteria performs
better when trained on the candidate sentences than when trained on sentences
randomly picked from the text, supporting the intuition that our method is able
to accurately identify study descriptors.
