Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

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 tools to search access data. contents relationships between measurements in database are continuously augmented revised stay current with astrophysics literature new sky surveys. conventional process of distilling extracting data from involves human experts review journal articles determine if an article extragalactic nature, so, what types it contains. This both labor intensive unsustainable, especially given ever-increasing number publications each year. We present here machine learning (ML) approach developed integrated into NED production pipeline help automate classification topics their content inclusion NED. show this ML application can successfully reproduce classifications expert accuracy over 90% fraction time takes human, allowing us focus expertise on tasks more difficult automate.