@inproceedings{zhang-coltekin-2024-tubingen,
title = {{T}{\"u}bingen-{CL} at {S}em{E}val-2024 Task 1: Ensemble Learning for Semantic Relatedness Estimation},
author = {Zhang, Leixin and
{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.147",
doi = "10.18653/v1/2024.semeval-1.147",
pages = "1019--1025",
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.",
}
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%0 Conference Proceedings
%T Tübingen-CL at SemEval-2024 Task 1: Ensemble Learning for Semantic Relatedness Estimation
%A Zhang, Leixin
%A Çöltekin, Çağrı
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhang-coltekin-2024-tubingen
%X 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.
%R 10.18653/v1/2024.semeval-1.147
%U https://aclanthology.org/2024.semeval-1.147
%U https://doi.org/10.18653/v1/2024.semeval-1.147
%P 1019-1025
Markdown (Informal)
[Tübingen-CL at SemEval-2024 Task 1: Ensemble Learning for Semantic Relatedness Estimation](https://aclanthology.org/2024.semeval-1.147) (Zhang & Çöltekin, SemEval 2024)
ACL