Abstract
The paper introduces our system for SemEval-2024 Task 1, which aims to predict the relatedness of sentence pairs. Operating under the hypothesis that semantic relatedness is a broader concept that extends beyond mere similarity of sentences, our approach seeks to identify useful features for relatedness estimation. We employ an ensemble approach integrating various systems, including statistical textual features and outputs of deep learning models to predict relatedness scores. The findings suggest that semantic relatedness can be inferred from various sources and ensemble models outperform many individual systems in estimating semantic relatedness.- Anthology ID:
- 2024.semeval-1.147
- 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:
- 1019–1025
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.147
- DOI:
- Cite (ACL):
- Leixin Zhang and Çağrı Çöltekin. 2024. Tübingen-CL at SemEval-2024 Task 1: Ensemble Learning for Semantic Relatedness Estimation. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1019–1025, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Tübingen-CL at SemEval-2024 Task 1: Ensemble Learning for Semantic Relatedness Estimation (Zhang & Çöltekin, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.147.pdf