Abstract
This paper presents the SATLab participation in SemEval 2024 Task 1 on Semantic Textual Relatedness. The proposed system predicts semantic relatedness by means of the Euclidean distance between the character ngram frequencies in the two sentences to evaluate. It employs no external resources, nor information from other instances present in the material. The system performs well, coming first in five of the twelve languages. However, there is little difference between the best systems.- Anthology ID:
- 2024.semeval-1.16
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 95–100
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.16
- DOI:
- 10.18653/v1/2024.semeval-1.16
- Cite (ACL):
- Yves Bestgen. 2024. SATLab at SemEval-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 95–100, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- SATLab at SemEval-2024 Task 1: A Fully Instance-Specific Approach for Semantic Textual Relatedness Prediction (Bestgen, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.semeval-1.16.pdf