TDAC, the First Time-Domain Astrophysics Corpus: Analysis and First Experiments on Named Entity Recognition

Atilla Kaan Alkan (1, 2, 3) , Cyril Grouin (2, 4) , Fabian Schüssler (3) , Pierre Zweigenbaum (4, 2)

1 ILES - Information, Langue Ecrite et Signée
2 STL - Sciences et Technologies des Langues - LISN
3 IRFU - Institut de Recherches sur les lois Fondamentales de l'Univers
4 ILES - Information, Langue Ecrite et Signée

The increased interest in time-domain astronomy over the last decades has resulted in a substantial increase in observation report publication leading to a saturation of how astrophysicists read, analyze and classify information. Due to the short life span of the detected astronomical events, information related to the characterization of new phenomena has to be communicated and analyzed very rapidly to allow other observatories to react and conduct their follow-up observations. This paper introduces TDAC: a Time-Domain Astrophysics Corpus. TDAC is the first corpus based on astrophysical observation reports. We also present the NLP experiments we made for named entity recognition based on annotations we made and annotations from the WIESP DEAL shared task.

Keywords: Natural Language Processing; Information Extraction; Named Entity Recognition; Annotated Corpus; Astrophysics; GCN Circulars; Astronomer's Telegram; AstroNotes

Domains: Document and Text Processing; Astrophysics [astro-ph]

Atilla Kaan Alkan, Cyril Grouin, Fabian Schüssler, Pierre Zweigenbaum. TDAC, the First Time-Domain Astrophysics Corpus: Analysis and First Experiments on Named Entity Recognition. Workshop on Information Extraction from Scientific Publications, Nov 2022, Taipei (Online), Taiwan. ⟨hal-04046837⟩