  The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service
that combines fundamental multi-wavelength information for known objects beyond
the Milky Way and provides value-added, derived quantities and tools to search
and access the data. The contents and relationships between measurements in the
database are continuously augmented and revised to stay current with
astrophysics literature and new sky surveys. The conventional process of
distilling and extracting data from the literature involves human experts to
review the journal articles and determine if an article is of extragalactic
nature, and if so, what types of data it contains. This is both labor intensive
and unsustainable, especially given the ever-increasing number of publications
each year. We present here a machine learning (ML) approach developed and
integrated into the NED production pipeline to help automate the classification
of journal article topics and their data content for inclusion into NED. We
show that this ML application can successfully reproduce the classifications of
a human expert to an accuracy of over 90% in a fraction of the time it takes a
human, allowing us to focus human expertise on tasks that are more difficult to
automate.
