An Annotation Scheme for Factuality and Its Application to Parliamentary Proceedings

Gili Goldin, Shira Wigderson, Ella Rabinovich, Shuly Wintner


Abstract
Factuality assesses the extent to which a language utterance relates to real-world information; it determines whether utterances correspond to facts, possibilities, or imaginary situations, and as such, it is instrumental for fact checking. Factuality is a complex notion that relies on multiple linguistic signals, and has been studied in various disciplines. We present a complex, multi-faceted annotation scheme of factuality that combines concepts from a variety of previous works. We developed the scheme for Hebrew, but we trust that it can be adapted to other languages. We also present a set of almost 5,000 sentences in the domain of parliamentary discourse that we manually annotated according to this scheme. We report on inter-annotator agreement, and experiment with various approaches to automatically predict (some features of) the scheme, in order to extend the annotation to a large corpus.
Anthology ID:
2025.ranlp-1.49
Volume:
Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
Month:
September
Year:
2025
Address:
Varna, Bulgaria
Editors:
Galia Angelova, Maria Kunilovskaya, Marie Escribe, Ruslan Mitkov
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
403–412
Language:
URL:
https://aclanthology.org/2025.ranlp-1.49/
DOI:
Bibkey:
Cite (ACL):
Gili Goldin, Shira Wigderson, Ella Rabinovich, and Shuly Wintner. 2025. An Annotation Scheme for Factuality and Its Application to Parliamentary Proceedings. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 403–412, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
An Annotation Scheme for Factuality and Its Application to Parliamentary Proceedings (Goldin et al., RANLP 2025)
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PDF:
https://aclanthology.org/2025.ranlp-1.49.pdf