Enriching a Time-Domain Astrophysics Corpus with Named Entity, Coreference and Astrophysical Relationship Annotations
Atilla Kaan Alkan, Felix Grezes, Cyril Grouin, Fabian Schussler, Pierre Zweigenbaum
Abstract
Interest in Astrophysical Natural Language Processing (NLP) has increased recently, fueled by the development of specialized language models for information extraction. However, the scarcity of annotated resources for this domain is still a significant challenge. Most existing corpora are limited to Named Entity Recognition (NER) tasks, leaving a gap in resource diversity. To address this gap and facilitate a broader spectrum of NLP research in astrophysics, we introduce astroECR, an extension of our previously built Time-Domain Astrophysics Corpus (TDAC). Our contributions involve expanding it to cover named entities, coreferences, annotations related to astrophysical relationships, and normalizing celestial object names. We showcase practical utility through baseline models for four NLP tasks and provide the research community access to our corpus, code, and models.
BibTeX
@inproceedings{alkan-etal-2024-enriching,
title = "Enriching a Time-Domain Astrophysics Corpus with Named Entity, Coreference and Astrophysical Relationship Annotations",
author = "Alkan, Atilla Kaan and
Grezes, Felix and
Grouin, Cyril and
Schussler, Fabian and
Zweigenbaum, Pierre",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.545/",
pages = "6177--6188"
}