Search Results for author: Wassim El-Hajj

Found 15 papers, 4 papers with code

Arabic Corpora for Credibility Analysis

no code implementations LREC 2016 Ayman Al Zaatari, Rim El Ballouli, Shady ELbassouni, Wassim El-Hajj, Hazem Hajj, Khaled Shaban, Nizar Habash, Emad Yahya

We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic.

BIG-bench Machine Learning General Classification

EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet

no code implementations SEMEVAL 2018 Gilbert Badaro, Hussein Jundi, Hazem Hajj, Wassim El-Hajj

We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood.

Collaborative Filtering Emotion Classification +3

Assessing Arabic Weblog Credibility via Deep Co-learning

no code implementations WS 2019 Chadi Helwe, Shady Elbassuoni, Ayman Al Zaatari, Wassim El-Hajj

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.

BIG-bench Machine Learning

Improved Generalization of Arabic Text Classifiers

no code implementations WS 2019 Alaa Khaddaj, Hazem Hajj, Wassim El-Hajj

While transfer learning for text has been very active in the English language, progress in Arabic has been slow, including the use of Domain Adaptation (DA).

Domain Adaptation Transfer Learning

hULMonA: The Universal Language Model in Arabic

1 code implementation WS 2019 Obeida ElJundi, Wissam Antoun, Nour El Droubi, Hazem Hajj, Wassim El-Hajj, Khaled Shaban

Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.

Arabic Sentiment Analysis General Classification +4

Creating Speech-to-Speech Corpus from Dubbed Series

1 code implementation7 Mar 2022 Massa Baali, Wassim El-Hajj, Ahmed Ali

We propose an unsupervised approach to construct speech-to-speech corpus, aligned on short segment levels, to produce a parallel speech corpus in the source- and target- languages.

Machine Translation speech-recognition +1

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