@inproceedings{de-francony-etal-2019-hierarchical,
title = "Hierarchical Deep Learning for {A}rabic Dialect Identification",
author = "de Francony, Gael and
Guichard, Victor and
Joshi, Praveen and
Afli, Haithem and
Bouchekif, Abdessalam",
editor = "El-Hajj, Wassim and
Belguith, Lamia Hadrich and
Bougares, Fethi and
Magdy, Walid and
Zitouni, Imed and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4631",
doi = "10.18653/v1/W19-4631",
pages = "249--253",
abstract = "In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 {\%} on the subtask evaluation dataset.",
}
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<abstract>In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 % on the subtask evaluation dataset.</abstract>
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%0 Conference Proceedings
%T Hierarchical Deep Learning for Arabic Dialect Identification
%A de Francony, Gael
%A Guichard, Victor
%A Joshi, Praveen
%A Afli, Haithem
%A Bouchekif, Abdessalam
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F de-francony-etal-2019-hierarchical
%X In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 % on the subtask evaluation dataset.
%R 10.18653/v1/W19-4631
%U https://aclanthology.org/W19-4631
%U https://doi.org/10.18653/v1/W19-4631
%P 249-253
Markdown (Informal)
[Hierarchical Deep Learning for Arabic Dialect Identification](https://aclanthology.org/W19-4631) (de Francony et al., WANLP 2019)
ACL
- Gael de Francony, Victor Guichard, Praveen Joshi, Haithem Afli, and Abdessalam Bouchekif. 2019. Hierarchical Deep Learning for Arabic Dialect Identification. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 249–253, Florence, Italy. Association for Computational Linguistics.