@inproceedings{lopez-ponce-etal-2024-gil,
title = "{GIL}-{IIMAS} {UNAM} at {S}em{E}val-2024 Task 1: {SAND}: An In Depth Analysis of Semantic Relatedness Using Regression and Similarity Characteristics",
author = "Lopez-ponce, Francisco and
Cadena, {\'A}ngel and
Salas-jimenez, Karla and
Bel-enguix, Gemma and
Preciado-m{\'a}rquez, David",
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-1.186/",
doi = "10.18653/v1/2024.semeval-1.186",
pages = "1288--1292",
abstract = "The STR shared task aims at detecting the degree of semantic relatedness between sentence pairs in multiple languages. Semantic relatedness relies on elements such as topic similarity, point of view agreement, entailment, and even human intuition, making it a broader field than sentence similarity. The GIL-IIMAS UNAM team proposes a model based in the SAND characteristics composition (Sentence Transformers, AnglE Embeddings, N-grams, Sentence Length Difference coefficient) and classical regression algorithms. This model achieves a 0.83 Spearman Correlation score in the English test, and a 0.73 in the Spanish counterpart, finishing just above the SemEval baseline in English, and second place in Spanish."
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%0 Conference Proceedings
%T GIL-IIMAS UNAM at SemEval-2024 Task 1: SAND: An In Depth Analysis of Semantic Relatedness Using Regression and Similarity Characteristics
%A Lopez-ponce, Francisco
%A Cadena, Ángel
%A Salas-jimenez, Karla
%A Bel-enguix, Gemma
%A Preciado-márquez, David
%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 lopez-ponce-etal-2024-gil
%X The STR shared task aims at detecting the degree of semantic relatedness between sentence pairs in multiple languages. Semantic relatedness relies on elements such as topic similarity, point of view agreement, entailment, and even human intuition, making it a broader field than sentence similarity. The GIL-IIMAS UNAM team proposes a model based in the SAND characteristics composition (Sentence Transformers, AnglE Embeddings, N-grams, Sentence Length Difference coefficient) and classical regression algorithms. This model achieves a 0.83 Spearman Correlation score in the English test, and a 0.73 in the Spanish counterpart, finishing just above the SemEval baseline in English, and second place in Spanish.
%R 10.18653/v1/2024.semeval-1.186
%U https://aclanthology.org/2024.semeval-1.186/
%U https://doi.org/10.18653/v1/2024.semeval-1.186
%P 1288-1292
Markdown (Informal)
[GIL-IIMAS UNAM at SemEval-2024 Task 1: SAND: An In Depth Analysis of Semantic Relatedness Using Regression and Similarity Characteristics](https://aclanthology.org/2024.semeval-1.186/) (Lopez-ponce et al., SemEval 2024)
ACL