@inproceedings{eponon-ramos-perez-2024-pinealai,
title = "Pinealai at {S}em{E}val-2024 Task 1: Exploring Semantic Relatedness Prediction using Syntactic, {TF}-{IDF}, and Distance-Based Features.",
author = "Eponon, Alex and
Ramos Perez, Luis",
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-1.135",
doi = "10.18653/v1/2024.semeval-1.135",
pages = "935--939",
abstract = "The central aim of this experiment is to establish a system proficient in predicting semantic relatedness between pairs of English texts. Additionally, the study seeks to delve into diverse features capable of enhancing the ability of models to identify semantic relatedness within given sentences. Several strategies have been used that combine TF-IDF, syntactic features, and similarity measures to train machine learning to predict semantic relatedness between pairs of sentences. The results obtained were above the baseline with an approximate Spearman score of 0.84.",
}
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%0 Conference Proceedings
%T Pinealai at SemEval-2024 Task 1: Exploring Semantic Relatedness Prediction using Syntactic, TF-IDF, and Distance-Based Features.
%A Eponon, Alex
%A Ramos Perez, Luis
%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 eponon-ramos-perez-2024-pinealai
%X The central aim of this experiment is to establish a system proficient in predicting semantic relatedness between pairs of English texts. Additionally, the study seeks to delve into diverse features capable of enhancing the ability of models to identify semantic relatedness within given sentences. Several strategies have been used that combine TF-IDF, syntactic features, and similarity measures to train machine learning to predict semantic relatedness between pairs of sentences. The results obtained were above the baseline with an approximate Spearman score of 0.84.
%R 10.18653/v1/2024.semeval-1.135
%U https://aclanthology.org/2024.semeval-1.135
%U https://doi.org/10.18653/v1/2024.semeval-1.135
%P 935-939
Markdown (Informal)
[Pinealai at SemEval-2024 Task 1: Exploring Semantic Relatedness Prediction using Syntactic, TF-IDF, and Distance-Based Features.](https://aclanthology.org/2024.semeval-1.135) (Eponon & Ramos Perez, SemEval 2024)
ACL