@inproceedings{alami-etal-2023-um6p,
title = "{UM}6{P} at {S}em{E}val-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences",
author = "Alami, Hamza and
Benlahbib, Abdessamad and
El Mahdaouy, Abdelkader and
Berrada, Ismail",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.118",
doi = "10.18653/v1/2023.semeval-1.118",
pages = "856--861",
abstract = "This paper presents our proposed method for english documents genre classification in the context of SemEval 2023 task 3, subtask 1. Our method use ensemble technique to combine four distinct models predictions: Longformer, RoBERTa, GCN, and a sentences number-based model. Each model is optimized on simple objectives and easy to grasp. We provide snippets of code that define each model to make the reading experience better. Our method ranked 12th in documents genre classification for english texts.",
}
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%0 Conference Proceedings
%T UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences
%A Alami, Hamza
%A Benlahbib, Abdessamad
%A El Mahdaouy, Abdelkader
%A Berrada, Ismail
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F alami-etal-2023-um6p
%X This paper presents our proposed method for english documents genre classification in the context of SemEval 2023 task 3, subtask 1. Our method use ensemble technique to combine four distinct models predictions: Longformer, RoBERTa, GCN, and a sentences number-based model. Each model is optimized on simple objectives and easy to grasp. We provide snippets of code that define each model to make the reading experience better. Our method ranked 12th in documents genre classification for english texts.
%R 10.18653/v1/2023.semeval-1.118
%U https://aclanthology.org/2023.semeval-1.118
%U https://doi.org/10.18653/v1/2023.semeval-1.118
%P 856-861
Markdown (Informal)
[UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences](https://aclanthology.org/2023.semeval-1.118) (Alami et al., SemEval 2023)
ACL