@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}