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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

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 · ORCID
classification cs.CLastro-ph.IM
keywords astrobertlanguageastronomyastrophysicsdatasetmissionmodelplanck
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

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  1. Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics

    cs.CL 2024-11 conditional novelty 5.0 of 10

    This paper introduces Astro-HEP-BERT, a BERT model adapted to astrophysics and high-energy physics text, plus a large arXiv-based corpus, as a low-cost tool for studying conceptual change in science.

  2. Meaning at the Planck scale? Contextualized word embeddings for doing history, philosophy, and sociology of science

    cs.CL 2024-11 conditional novelty 5.0 of 10

    Domain-adapted BERT models distinguish senses of 'Planck' better than general models, and reveal the rise of the Planck mission meaning in physics papers.

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