TED-EL: A Corpus for Speech Entity Linking

Silin Li, Ruoyu Song, Tianwei Lan, Zeming Liu, Yuhang Guo


Abstract
Speech entity linking amis to recognize mentions from speech and link them to entities in knowledge bases. Previous work on entity linking mainly focuses on visual context and text context. In contrast, speech entity linking focuses on audio context. In this paper, we first propose the speech entity linking task. To facilitate the study of this task, we propose the first speech entity linking dataset, TED-EL. Our corpus is a high-quality, human-annotated, audio, text, and mention-entity pair parallel dataset derived from Technology, Entertainment, Design (TED) talks and includes a wide range of entity types (24 types). Based on TED-EL, we designed two types of models: ranking-based and generative speech entity linking models. We conducted experiments on the TED-EL dataset for both types of models. The results show that the ranking-based models outperform the generative models, achieving an F1 score of 60.68%.
Anthology ID:
2024.lrec-main.1365
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
15721–15731
Language:
URL:
https://aclanthology.org/2024.lrec-main.1365
DOI:
Bibkey:
Cite (ACL):
Silin Li, Ruoyu Song, Tianwei Lan, Zeming Liu, and Yuhang Guo. 2024. TED-EL: A Corpus for Speech Entity Linking. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15721–15731, Torino, Italia. ELRA and ICCL.
Cite (Informal):
TED-EL: A Corpus for Speech Entity Linking (Li et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.1365.pdf