@inproceedings{helwe-etal-2019-assessing,
title = "Assessing {A}rabic Weblog Credibility via Deep Co-learning",
author = "Helwe, Chadi and
Elbassuoni, Shady and
Al Zaatari, Ayman and
El-Hajj, Wassim",
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/W19-4614",
doi = "10.18653/v1/W19-4614",
pages = "130--136",
abstract = "Assessing the credibility of online content has garnered a lot of attention lately. We focus on one such type of online content, namely weblogs or blogs for short. Some recent work attempted the task of automatically assessing the credibility of blogs, typically via machine learning. However, in the case of Arabic blogs, there are hardly any datasets available that can be used to train robust machine learning models for this difficult task. To overcome the lack of sufficient training data, we propose deep co-learning, a semi-supervised end-to-end deep learning approach to assess the credibility of Arabic blogs. In deep co-learning, multiple weak deep neural network classifiers are trained using a small labeled dataset, and each using a different view of the data. Each one of these classifiers is then used to classify unlabeled data, and its prediction is used to train the other classifiers in a semi-supervised fashion. We evaluate our deep co-learning approach on an Arabic blogs dataset, and we report significant improvements in performance compared to many baselines including fully-supervised deep learning models as well as ensemble models.",
}
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<abstract>Assessing the credibility of online content has garnered a lot of attention lately. We focus on one such type of online content, namely weblogs or blogs for short. Some recent work attempted the task of automatically assessing the credibility of blogs, typically via machine learning. However, in the case of Arabic blogs, there are hardly any datasets available that can be used to train robust machine learning models for this difficult task. To overcome the lack of sufficient training data, we propose deep co-learning, a semi-supervised end-to-end deep learning approach to assess the credibility of Arabic blogs. In deep co-learning, multiple weak deep neural network classifiers are trained using a small labeled dataset, and each using a different view of the data. Each one of these classifiers is then used to classify unlabeled data, and its prediction is used to train the other classifiers in a semi-supervised fashion. We evaluate our deep co-learning approach on an Arabic blogs dataset, and we report significant improvements in performance compared to many baselines including fully-supervised deep learning models as well as ensemble models.</abstract>
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%0 Conference Proceedings
%T Assessing Arabic Weblog Credibility via Deep Co-learning
%A Helwe, Chadi
%A Elbassuoni, Shady
%A Al Zaatari, Ayman
%A El-Hajj, Wassim
%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 helwe-etal-2019-assessing
%X Assessing the credibility of online content has garnered a lot of attention lately. We focus on one such type of online content, namely weblogs or blogs for short. Some recent work attempted the task of automatically assessing the credibility of blogs, typically via machine learning. However, in the case of Arabic blogs, there are hardly any datasets available that can be used to train robust machine learning models for this difficult task. To overcome the lack of sufficient training data, we propose deep co-learning, a semi-supervised end-to-end deep learning approach to assess the credibility of Arabic blogs. In deep co-learning, multiple weak deep neural network classifiers are trained using a small labeled dataset, and each using a different view of the data. Each one of these classifiers is then used to classify unlabeled data, and its prediction is used to train the other classifiers in a semi-supervised fashion. We evaluate our deep co-learning approach on an Arabic blogs dataset, and we report significant improvements in performance compared to many baselines including fully-supervised deep learning models as well as ensemble models.
%R 10.18653/v1/W19-4614
%U https://aclanthology.org/W19-4614
%U https://doi.org/10.18653/v1/W19-4614
%P 130-136
Markdown (Informal)
[Assessing Arabic Weblog Credibility via Deep Co-learning](https://aclanthology.org/W19-4614) (Helwe et al., WANLP 2019)
ACL
- Chadi Helwe, Shady Elbassuoni, Ayman Al Zaatari, and Wassim El-Hajj. 2019. Assessing Arabic Weblog Credibility via Deep Co-learning. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 130–136, Florence, Italy. Association for Computational Linguistics.