Available methods (Tkaczyk et al., 2018) rely on a painstaking tagging process which nonetheless only works with texts that are close to the original training dataset <ref xmlns="http://www.tei-c.org/ns/1.0" target="https://www.zotero.org/google-docs/?kol5EK">(Grennan & Beel, 2020)</ref>. Moreover, these methods require complex, black-box-like, and highly carbon-emitting computer power. In this presentation I will showcase a method that combines exploratory data analysis with readable machine learning results to automatically detect pages with reference lists on which NLP techniques such as Name Entity Recognition (NER) and fuzzy matching will be used to pair reference strings with richer metadata.