Building astroBERT, a language model for Astronomy & Astrophysics

Alberto Accomazzi; Carolyn S. Grant; Chris Tanner; Donna M. Thompson; Edwin Henneken; Felix Grezes; Golnaz Shapurian; Kelly E. Lockhart; Matthew R. Templeton; Michael J. Kurtz

Building astroBERT, a language model for Astronomy & Astrophysics

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arxiv 2112.00590 v1 pith:MN5GSSQH submitted 2021-12-01 cs.CL astro-ph.IM

Building astroBERT, a language model for Astronomy & Astrophysics

Felix Grezes , Sergi Blanco-Cuaresma , Alberto Accomazzi , Michael J. Kurtz , Golnaz Shapurian , Edwin Henneken , Carolyn S. Grant , Donna M. Thompson

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Roman Chyla Stephen McDonald Timothy W. Hostetler Matthew R. Templeton Kelly E. Lockhart Nemanja Martinovic Shinyi Chen Chris Tanner Pavlos Protopapas

This is my paper

classification cs.CL astro-ph.IM

keywords astrobertlanguageastronomyastrophysicsdatasetmissionmodelplanck

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The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e.g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search. For example, a query for "results from the Planck mission" should be able to distinguish between all the various meanings of Planck (person, mission, constant, institutions and more) without further clarification from the user. At ADS, we are applying modern machine learning and natural language processing techniques to our dataset of recent astronomy publications to train astroBERT, a deeply contextual language model based on research at Google. Using astroBERT, we aim to enrich the ADS dataset and improve its discoverability, and in particular we are developing our own named entity recognition tool. We present here our preliminary results and lessons learned.

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