@inproceedings{concas-etal-2025-ilostthecode,
title = "i{L}ost{T}he{C}ode at {S}em{E}val-2025 Task 10: Bottom-up Multilevel Classification of Narrative Taxonomies",
author = "Concas, Lorenzo Vittorio and
Sanguinetti, Manuela and
Atzori, Maurizio",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.85/",
pages = "607--616",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes the approach used to address the task of narrative classification, which has been proposed as a subtask of Task 10 on Multilingual Characterization and Extraction of Narratives from Online News at the SemEval 2025 campaign. The task consists precisely in assigning all relevant sub-narrative labels from a two-level taxonomy to a given news article in multiple languages (i.e., Bulgarian, English, Hindi, Portuguese and Russian). This involves performing both multi-label and multi-class classification. The model developed for this purpose uses multiple pretrained BERT-based models to create contextualized embeddings that are concatenated and then fed into a simple neural network to compute classification probabilities. Results on the official test set, evaluated using samples {\$}F{\_}1{\$}, range from {\$}0.15{\$} in Hindi (rank {\#}9) to {\$}0.41{\$} in Russian (rank {\#}3). Besides an overview of the system and the results obtained in the task, the paper also includes some additional experiments carried out after the evaluation phase along with a brief discussion of the observed errors."
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<abstract>This paper describes the approach used to address the task of narrative classification, which has been proposed as a subtask of Task 10 on Multilingual Characterization and Extraction of Narratives from Online News at the SemEval 2025 campaign. The task consists precisely in assigning all relevant sub-narrative labels from a two-level taxonomy to a given news article in multiple languages (i.e., Bulgarian, English, Hindi, Portuguese and Russian). This involves performing both multi-label and multi-class classification. The model developed for this purpose uses multiple pretrained BERT-based models to create contextualized embeddings that are concatenated and then fed into a simple neural network to compute classification probabilities. Results on the official test set, evaluated using samples $F_1$, range from $0.15$ in Hindi (rank #9) to $0.41$ in Russian (rank #3). Besides an overview of the system and the results obtained in the task, the paper also includes some additional experiments carried out after the evaluation phase along with a brief discussion of the observed errors.</abstract>
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%0 Conference Proceedings
%T iLostTheCode at SemEval-2025 Task 10: Bottom-up Multilevel Classification of Narrative Taxonomies
%A Concas, Lorenzo Vittorio
%A Sanguinetti, Manuela
%A Atzori, Maurizio
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F concas-etal-2025-ilostthecode
%X This paper describes the approach used to address the task of narrative classification, which has been proposed as a subtask of Task 10 on Multilingual Characterization and Extraction of Narratives from Online News at the SemEval 2025 campaign. The task consists precisely in assigning all relevant sub-narrative labels from a two-level taxonomy to a given news article in multiple languages (i.e., Bulgarian, English, Hindi, Portuguese and Russian). This involves performing both multi-label and multi-class classification. The model developed for this purpose uses multiple pretrained BERT-based models to create contextualized embeddings that are concatenated and then fed into a simple neural network to compute classification probabilities. Results on the official test set, evaluated using samples $F_1$, range from $0.15$ in Hindi (rank #9) to $0.41$ in Russian (rank #3). Besides an overview of the system and the results obtained in the task, the paper also includes some additional experiments carried out after the evaluation phase along with a brief discussion of the observed errors.
%U https://aclanthology.org/2025.semeval-1.85/
%P 607-616
Markdown (Informal)
[iLostTheCode at SemEval-2025 Task 10: Bottom-up Multilevel Classification of Narrative Taxonomies](https://aclanthology.org/2025.semeval-1.85/) (Concas et al., SemEval 2025)
ACL