Khaled Shaban

Also published as: Khaled Bashir Shaban


2019

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hULMonA: The Universal Language Model in Arabic
Obeida ElJundi | Wissam Antoun | Nour El Droubi | Hazem Hajj | Wassim El-Hajj | Khaled Shaban
Proceedings of the Fourth Arabic Natural Language Processing Workshop

Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis. The utilization of transfer learning (TL) has recently shown promising results for advancing accuracy of text classification in English. TL models are pre-trained on large corpora, and then fine-tuned on task-specific datasets. In particular, universal language models (ULMs), such as recently developed BERT, have achieved state-of-the-art results in various NLP tasks in English. In this paper, we hypothesize that similar success can be achieved for Arabic. The work aims at supporting the hypothesis by developing the first Universal Language Model in Arabic (hULMonA - حلمنا meaning our dream), demonstrating its use for Arabic classifications tasks, and demonstrating how a pre-trained multi-lingual BERT can also be used for Arabic. We then conduct a benchmark study to evaluate both ULM successes with Arabic sentiment analysis. 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.

2017

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OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model
Ramy Baly | Gilbert Badaro | Ali Hamdi | Rawan Moukalled | Rita Aoun | Georges El-Khoury | Ahmad Al Sallab | Hazem Hajj | Nizar Habash | Khaled Shaban | Wassim El-Hajj
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the “OMAM” systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D.

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CAT: Credibility Analysis of Arabic Content on Twitter
Rim El Ballouli | Wassim El-Hajj | Ahmad Ghandour | Shady Elbassuoni | Hazem Hajj | Khaled Shaban
Proceedings of the Third Arabic Natural Language Processing Workshop

Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author’s profile and timeline. To train and test CAT, we annotated for credibility a data set of 9,000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average F-measure. We also conducted experiments to highlight the importance of the user-based features as opposed to the content-based features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.

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A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models
Ramy Baly | Gilbert Badaro | Georges El-Khoury | Rawan Moukalled | Rita Aoun | Hazem Hajj | Wassim El-Hajj | Nizar Habash | Khaled Shaban
Proceedings of the Third Arabic Natural Language Processing Workshop

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic’s complex morphology.

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Methodical Evaluation of Arabic Word Embeddings
Mohammed Elrazzaz | Shady Elbassuoni | Khaled Shaban | Chadi Helwe
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of the embeddings based on two NLP tasks.

2016

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Arabic Corpora for Credibility Analysis
Ayman Al Zaatari | Rim El Ballouli | Shady ELbassouni | Wassim El-Hajj | Hazem Hajj | Khaled Shaban | Nizar Habash | Emad Yahya
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be deployed to build automatic credibility classifiers. However, as in the case with most supervised machine learning approaches, a sufficiently large and accurate training data must be available. In this paper, we focus on building a public Arabic corpus of blogs and microblogs that can be used for credibility classification. 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. We discuss our data acquisition approach and annotation process, provide rigid analysis on the annotated data and finally report some results on the effectiveness of our data for credibility classification.

2015

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Deep Learning Models for Sentiment Analysis in Arabic
Ahmad Al Sallab | Hazem Hajj | Gilbert Badaro | Ramy Baly | Wassim El Hajj | Khaled Bashir Shaban
Proceedings of the Second Workshop on Arabic Natural Language Processing

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A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets
Gilbert Badaro | Ramy Baly | Rana Akel | Linda Fayad | Jeffrey Khairallah | Hazem Hajj | Khaled Shaban | Wassim El-Hajj
Proceedings of the Second Workshop on Arabic Natural Language Processing