Abstract
In this work we present a system for multilingual olfactory information extraction covering six European languages, introducing new models to extract olfactory information from large amounts of text in a structured and scalable way. For the task we rely on a supervised multi-task approach to detect olfactory related text adopting a FrameNet-like structure, identifying the lexical units triggering the smell event and a related set of frame elements.- Anthology ID:
- 2024.lrec-main.1273
- 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:
- 14622–14627
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1273
- DOI:
- Cite (ACL):
- Stefano Menini. 2024. Semantic Frame Extraction in Multilingual Olfactory Events. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14622–14627, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Semantic Frame Extraction in Multilingual Olfactory Events (Menini, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1273.pdf