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The automatic identification of planetary feature names in astronomypublications presents numerous challenges. These features include craters,defined as roughly circular depressions resulting from impact or volcanicactivity; dorsas, which are elongate raised structures or wrinkle ridges; andlacus, small irregular patches of dark, smooth material on the Moon, referredto as "lake" (Planetary Names Working Group, n.d.). Many feature names overlapwith places or people's names that they are named after, for example, Syria,Tempe, Einstein, and Sagan, to name a few (U.S. Geological Survey, n.d.). Somefeature names have been used in many contexts, for instance, Apollo, which canrefer to mission, program, sample, astronaut, seismic, seismometers, core, era,data, collection, instrument, and station, in addition to the crater on theMoon. Some feature names can appear in the text as adjectives, like the lunarcraters Black, Green, and White. Some feature names in other contexts serve asdirections, like craters West and South on the Moon. Additionally, somefeatures share identical names across different celestial bodies, requiringdisambiguation, such as the Adams crater, which exists on both the Moon andMars. We present a multi-step pipeline combining rule-based filtering,statistical relevance analysis, part-of-speech (POS) tagging, named entityrecognition (NER) model, hybrid keyword harvesting, knowledge graph (KG)matching, and inference with a locally installed large language model (LLM) toreliably identify planetary names despite these challenges. When evaluated on adataset of astronomy papers from the Astrophysics Data System (ADS), thismethodology achieves an F1-score over 0.97 in disambiguating planetary featurenames.

Identifying Planetary Names in Astronomy Papers: A Multi-Step Approach