Wajdi Zaghouani


2021

pdf bib
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
Firoj Alam | Shaden Shaar | Fahim Dalvi | Hassan Sajjad | Alex Nikolov | Hamdy Mubarak | Giovanni Da San Martino | Ahmed Abdelali | Nadir Durrani | Kareem Darwish | Abdulaziz Al-Homaid | Wajdi Zaghouani | Tommaso Caselli | Gijs Danoe | Friso Stolk | Britt Bruntink | Preslav Nakov
Findings of the Association for Computational Linguistics: EMNLP 2021

With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.

pdf bib
Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
Shaden Shaar | Firoj Alam | Giovanni Da San Martino | Alex Nikolov | Wajdi Zaghouani | Preslav Nakov | Anna Feldman
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leaderboards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.

pdf bib
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Nizar Habash | Houda Bouamor | Hazem Hajj | Walid Magdy | Wajdi Zaghouani | Fethi Bougares | Nadi Tomeh | Ibrahim Abu Farha | Samia Touileb
Proceedings of the Sixth Arabic Natural Language Processing Workshop

pdf bib
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic
Ibrahim Abu Farha | Wajdi Zaghouani | Walid Magdy
Proceedings of the Sixth Arabic Natural Language Processing Workshop

This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic. The shared task has two subtasks: sarcasm detection (subtask 1) and sentiment analysis (subtask 2). This shared task aims to promote and bring attention to Arabic sarcasm detection, which is crucial to improve the performance in other tasks such as sentiment analysis. The dataset used in this shared task, namely ArSarcasm-v2, consists of 15,548 tweets labelled for sarcasm, sentiment and dialect. We received 27 and 22 submissions for subtasks 1 and 2 respectively. Most of the approaches relied on using and fine-tuning pre-trained language models such as AraBERT and MARBERT. The top achieved results for the sarcasm detection and sentiment analysis tasks were 0.6225 F1-score and 0.748 F1-PN respectively.

2020

pdf bib
Proceedings of the Fifth Arabic Natural Language Processing Workshop
Imed Zitouni | Muhammad Abdul-Mageed | Houda Bouamor | Fethi Bougares | Mahmoud El-Haj | Nadi Tomeh | Wajdi Zaghouani
Proceedings of the Fifth Arabic Natural Language Processing Workshop

pdf bib
Building a Corpus of Qatari Arabic Expressions
Sara Al-Mulla | Wajdi Zaghouani
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection

The current Arabic natural language processing resources are mainly build to address the Modern Standard Arabic (MSA), while we witnessed some scattered efforts to build resources for various Arabic dialects such as the Levantine and the Egyptian dialects. We observed a lack of resources for Gulf Arabic and especially the Qatari variety. In this paper, we present the first Qatari idioms and expression corpus of 1000 entries. The corpus was created from on-line and printed sources in addition to transcribed recorded interviews. The corpus covers various Qatari traditional expressions and idioms. To this end, audio recordings were collected from interviews and an online survey questionnaire was conducted to validate our data. This corpus aims to help advance the dialectal Arabic Speech and Natural Language Processing tools and applications for the Qatari dialect.

pdf bib
DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter
Ines Abbes | Wajdi Zaghouani | Omaima El-Hardlo | Faten Ashour
Proceedings of the 12th Language Resources and Evaluation Conference

Identifying irony in user-generated social media content has a wide range of applications; however to date Arabic content has received limited attention. To bridge this gap, this study builds a new open domain Arabic corpus annotated for irony detection. We query Twitter using irony-related hashtags to collect ironic messages, which are then manually annotated by two linguists according to our working definition of irony. Challenges which we have encountered during the annotation process reflect the inherent limitations of Twitter messages interpretation, as well as the complexity of Arabic and its dialects. Once published, our corpus will be a valuable free resource for developing open domain systems for automatic irony recognition in Arabic language and its dialects in social media text.

2019

pdf bib
A Fine-Grained Annotated Multi-Dialectal Arabic Corpus
Anis Charfi | Wajdi Zaghouani | Syed Hassan Mehdi | Esraa Mohamed
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

We present ARAP-Tweet 2.0, a corpus of 5 million dialectal Arabic tweets and 50 million words of about 3000 Twitter users from 17 Arab countries. Compared to the first version, the new corpus has significant improvements in terms of the data volume and the annotation quality. It is fully balanced with respect to dialect, gender, and three age groups: under 25 years, between 25 and 34, and 35 years and above. This paper describes the process of creating the corpus starting from gathering the dialectal phrases to find the users, to annotating their accounts and retrieving their tweets. We also report on the evaluation of the annotation quality using the inter-annotator agreement measures which were applied to the whole corpus and not just a subset. The obtained results were substantial with average Cohen’s Kappa values of 0.99, 0.92, and 0.88 for the annotation of gender, dialect, and age respectively. We also discuss some challenges encountered when developing this corpus.s.

pdf bib
Proceedings of the Fourth Arabic Natural Language Processing Workshop
Wassim El-Hajj | Lamia Hadrich Belguith | Fethi Bougares | Walid Magdy | Imed Zitouni | Nadi Tomeh | Mahmoud El-Haj | Wajdi Zaghouani
Proceedings of the Fourth Arabic Natural Language Processing Workshop

2018

pdf bib
Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification
Wajdi Zaghouani | Anis Charfi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction
Ossama Obeid | Salam Khalifa | Nizar Habash | Houda Bouamor | Wajdi Zaghouani | Kemal Oflazer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
The MADAR Arabic Dialect Corpus and Lexicon
Houda Bouamor | Nizar Habash | Mohammad Salameh | Wajdi Zaghouani | Owen Rambow | Dana Abdulrahim | Ossama Obeid | Salam Khalifa | Fadhl Eryani | Alexander Erdmann | Kemal Oflazer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Unified Guidelines and Resources for Arabic Dialect Orthography
Nizar Habash | Fadhl Eryani | Salam Khalifa | Owen Rambow | Dana Abdulrahim | Alexander Erdmann | Reem Faraj | Wajdi Zaghouani | Houda Bouamor | Nasser Zalmout | Sara Hassan | Faisal Al-Shargi | Sakhar Alkhereyf | Basma Abdulkareem | Ramy Eskander | Mohammad Salameh | Hind Saddiki
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

pdf bib
Proceedings of the Third Arabic Natural Language Processing Workshop
Nizar Habash | Mona Diab | Kareem Darwish | Wassim El-Hajj | Hend Al-Khalifa | Houda Bouamor | Nadi Tomeh | Mahmoud El-Haj | Wajdi Zaghouani
Proceedings of the Third Arabic Natural Language Processing Workshop

2016

pdf bib
Using Ambiguity Detection to Streamline Linguistic Annotation
Wajdi Zaghouani | Abdelati Hawwari | Sawsan Alqahtani | Houda Bouamor | Mahmoud Ghoneim | Mona Diab | Kemal Oflazer
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

Arabic writing is typically underspecified for short vowels and other markups, referred to as diacritics. In addition to the lexical ambiguity exhibited in most languages, the lack of diacritics in written Arabic adds another layer of ambiguity which is an artifact of the orthography. In this paper, we present the details of three annotation experimental conditions designed to study the impact of automatic ambiguity detection, on annotation speed and quality in a large scale annotation project.

pdf bib
Building an Arabic Machine Translation Post-Edited Corpus: Guidelines and Annotation
Wajdi Zaghouani | Nizar Habash | Ossama Obeid | Behrang Mohit | Houda Bouamor | Kemal Oflazer
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present our guidelines and annotation procedure to create a human corrected machine translated post-edited corpus for the Modern Standard Arabic. Our overarching goal is to use the annotated corpus to develop automatic machine translation post-editing systems for Arabic that can be used to help accelerate the human revision process of translated texts. The creation of any manually annotated corpus usually presents many challenges. In order to address these challenges, we created comprehensive and simplified annotation guidelines which were used by a team of five annotators and one lead annotator. In order to ensure a high annotation agreement between the annotators, multiple training sessions were held and regular inter-annotator agreement measures were performed to check the annotation quality. The created corpus of manual post-edited translations of English to Arabic articles is the largest to date for this language pair.

pdf bib
Guidelines and Framework for a Large Scale Arabic Diacritized Corpus
Wajdi Zaghouani | Houda Bouamor | Abdelati Hawwari | Mona Diab | Ossama Obeid | Mahmoud Ghoneim | Sawsan Alqahtani | Kemal Oflazer
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres. The target size of the annotated corpus is 2 million words. We summarize the guidelines and describe issues encountered during the training of the annotators. We also discuss the challenges posed by the complexity of the Arabic language and how they are addressed. Finally, we present the diacritization annotation procedure and detail the quality of the resulting annotations.

pdf bib
Applying the Cognitive Machine Translation Evaluation Approach to Arabic
Irina Temnikova | Wajdi Zaghouani | Stephan Vogel | Nizar Habash
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The goal of the cognitive machine translation (MT) evaluation approach is to build classifiers which assign post-editing effort scores to new texts. The approach helps estimate fair compensation for post-editors in the translation industry by evaluating the cognitive difficulty of post-editing MT output. The approach counts the number of errors classified in different categories on the basis of how much cognitive effort they require in order to be corrected. In this paper, we present the results of applying an existing cognitive evaluation approach to Modern Standard Arabic (MSA). We provide a comparison of the number of errors and categories of errors in three MSA texts of different MT quality (without any language-specific adaptation), as well as a comparison between MSA texts and texts from three Indo-European languages (Russian, Spanish, and Bulgarian), taken from a previous experiment. The results show how the error distributions change passing from the MSA texts of worse MT quality to MSA texts of better MT quality, as well as a similarity in distinguishing the texts of better MT quality for all four languages.

2015

pdf bib
Correction Annotation for Non-Native Arabic Texts: Guidelines and Corpus
Wajdi Zaghouani | Nizar Habash | Houda Bouamor | Alla Rozovskaya | Behrang Mohit | Abeer Heider | Kemal Oflazer
Proceedings of The 9th Linguistic Annotation Workshop

pdf bib
The Second QALB Shared Task on Automatic Text Correction for Arabic
Alla Rozovskaya | Houda Bouamor | Nizar Habash | Wajdi Zaghouani | Ossama Obeid | Behrang Mohit
Proceedings of the Second Workshop on Arabic Natural Language Processing

pdf bib
A Pilot Study on Arabic Multi-Genre Corpus Diacritization
Houda Bouamor | Wajdi Zaghouani | Mona Diab | Ossama Obeid | Kemal Oflazer | Mahmoud Ghoneim | Abdelati Hawwari
Proceedings of the Second Workshop on Arabic Natural Language Processing

pdf bib
SAHSOH@QALB-2015 Shared Task: A Rule-Based Correction Method of Common Arabic Native and Non-Native Speakers’ Errors
Wajdi Zaghouani | Taha Zerrouki | Amar Balla
Proceedings of the Second Workshop on Arabic Natural Language Processing

pdf bib
Generating acceptable Arabic Core Vocabularies and Symbols for AAC users
E.A. Draffan | Mike Wald | Nawar Halabi | Ouadie Sabia | Wajdi Zaghouani | Amatullah Kadous | Amal Idris | Nadine Zeinoun | David Banes | Dana Lawand
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

2014

pdf bib
The First QALB Shared Task on Automatic Text Correction for Arabic
Behrang Mohit | Alla Rozovskaya | Nizar Habash | Wajdi Zaghouani | Ossama Obeid
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

pdf bib
CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction
Serena Jeblee | Houda Bouamor | Wajdi Zaghouani | Kemal Oflazer
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

pdf bib
Can Crowdsourcing be used for Effective Annotation of Arabic?
Wajdi Zaghouani | Kais Dukes
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Crowdsourcing has been used recently as an alternative to traditional costly annotation by many natural language processing groups. In this paper, we explore the use of Amazon Mechanical Turk (AMT) in order to assess the feasibility of using AMT workers (also known as Turkers) to perform linguistic annotation of Arabic. We used a gold standard data set taken from the Quran corpus project annotated with part-of-speech and morphological information. An Arabic language qualification test was used to filter out potential non-qualified participants. Two experiments were performed, a part-of-speech tagging task in where the annotators were asked to choose a correct word-category from a multiple choice list and case ending identification task. The results obtained so far showed that annotating Arabic grammatical case is harder than POS tagging, and crowdsourcing for Arabic linguistic annotation requiring expert annotators could be not as effective as other crowdsourcing experiments requiring less expertise and qualifications.

pdf bib
Large Scale Arabic Error Annotation: Guidelines and Framework
Wajdi Zaghouani | Behrang Mohit | Nizar Habash | Ossama Obeid | Nadi Tomeh | Alla Rozovskaya | Noura Farra | Sarah Alkuhlani | Kemal Oflazer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present annotation guidelines and a web-based annotation framework developed as part of an effort to create a manually annotated Arabic corpus of errors and corrections for various text types. Such a corpus will be invaluable for developing Arabic error correction tools, both for training models and as a gold standard for evaluating error correction algorithms. We summarize the guidelines we created. We also describe issues encountered during the training of the annotators, as well as problems that are specific to the Arabic language that arose during the annotation process. Finally, we present the annotation tool that was developed as part of this project, the annotation pipeline, and the quality of the resulting annotations.

2013

pdf bib
A Web-based Annotation Framework For Large-Scale Text Correction
Ossama Obeid | Wajdi Zaghouani | Behrang Mohit | Nizar Habash | Kemal Oflazer | Nadi Tomeh
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations

2012

pdf bib
Developing ARET: An NLP-based Educational Tool Set for Arabic Reading Enhancement
Mohammed Maamouri | Wajdi Zaghouani | Violetta Cavalli-Sforza | Dave Graff | Mike Ciul
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

pdf bib
A Pilot PropBank Annotation for Quranic Arabic
Wajdi Zaghouani | Abdelati Hawwari | Mona Diab
Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature

2010

pdf bib
L’intégration d’un outil de repérage d’entités nommées pour la langue arabe dans un système de veille
Wajdi Zaghouani
Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

Dans cette démonstration, nous présentons l’implémentation d’un outil de repérage d’entités nommées à base de règle pour la langue arabe dans le système de veille médiatique EMM (Europe Media Monitor).

pdf bib
The Revised Arabic PropBank
Wajdi Zaghouani | Mona Diab | Aous Mansouri | Sameer Pradhan | Martha Palmer
Proceedings of the Fourth Linguistic Annotation Workshop

pdf bib
From Speech to Trees: Applying Treebank Annotation to Arabic Broadcast News
Mohamed Maamouri | Ann Bies | Seth Kulick | Wajdi Zaghouani | Dave Graff | Mike Ciul
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The Arabic Treebank (ATB) Project at the Linguistic Data Consortium (LDC) has embarked on a large corpus of Broadcast News (BN) transcriptions, and this has led to a number of new challenges for the data processing and annotation procedures that were originally developed for Arabic newswire text (ATB1, ATB2 and ATB3). The corpus requirements currently posed by the DARPA GALE Program, including English translation of Arabic BN transcripts, word-level alignment of Arabic and English data, and creation of a corresponding English Treebank, place significant new constraints on ATB corpus creation, and require careful coordination among a wide assortment of concurrent activities and participants. Nonetheless, in spite of the new challenges posed by BN data, the ATB’s newly improved pipeline and revised annotation guidelines for newswire have proven to be robust enough that very few changes were necessary to account for the new genre of data. This paper presents the points where some adaptation has been necessary, and the overall pipeline as used in the production of BN ATB data.

pdf bib
Adapting a resource-light highly multilingual Named Entity Recognition system to Arabic
Wajdi Zaghouani | Bruno Pouliquen | Mohamed Ebrahim | Ralf Steinberger
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We present a fully functional Arabic information extraction (IE) system that is used to analyze large volumes of news texts every day to extract the named entity (NE) types person, organization, location, date and number, as well as quotations (direct reported speech) by and about people. The Named Entity Recognition (NER) system was not developed for Arabic, but - instead - a highly multilingual, almost language-independent NER system was adapted to also cover Arabic. The Semitic language Arabic substantially differs from the Indo-European and Finno-Ugric languages currently covered. This paper thus describes what Arabic language-specific resources had to be developed and what changes needed to be made to the otherwise language-independent rule set in order to be applicable to the Arabic language. The achieved evaluation results are generally satisfactory, but could be improved for certain entity types. The results of the IE tools can be seen on the Arabic pages of the freely accessible Europe Media Monitor (EMM) application NewsExplorer, which can be found at http://press.jrc.it/overview.html.

2008

pdf bib
A Pilot Arabic Propbank
Martha Palmer | Olga Babko-Malaya | Ann Bies | Mona Diab | Mohamed Maamouri | Aous Mansouri | Wajdi Zaghouani
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we present the details of creating a pilot Arabic proposition bank (Propbank). Propbanks exist for both English and Chinese. However the morphological and syntactic expression of linguistic phenomena in Arabic yields a very different type of process in creating an Arabic propbank. Hence, we highlight those characteristics of Arabic that make creating a propbank for the language a different challenge compared to the creation of an English Propbank.We believe that many of the lessons learned in dealing with Arabic could generalise to other languages that exhibit equally rich morphology and relatively free word order.

2006

pdf bib
Evaluation of multilingual text alignment systems: the ARCADE II project
Yun-Chuang Chiao | Olivier Kraif | Dominique Laurent | Thi Minh Huyen Nguyen | Nasredine Semmar | François Stuck | Jean Véronis | Wajdi Zaghouani
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes the ARCADE II project, concerned with the evaluation of parallel text alignment systems. The ARCADE II project aims at exploring the techniques of multilingual text alignment through a fine evaluation of the existing techniques and the development of new alignment methods. The evaluation campaign consists of two tracks devoted to the evaluation of alignment at sentence and word level respectively. It differs from ARCADE I in the multilingual aspect and the investigation of lexical alignment.

pdf bib
Geocoding Multilingual Texts: Recognition, Disambiguation and Visualisation
Bruno Pouliquen | Marco Kimler | Ralf Steinberger | Camelia Ignat | Tamara Oellinger | Ken Blackler | Flavio Fluart | Wajdi Zaghouani | Anna Widiger | Ann-Charlotte Forslund | Clive Best
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We are presenting a method to recognise geographical references in free text. Our tool must work on various languages with a minimum of language-dependent resources, except a gazetteer. The main difficulty is to disambiguate these place names by distinguishing places from persons and by selecting the most likely place out of a list of homographic place names world-wide. The system uses a number of language-independent clues and heuristics to disambiguate place name homographs. The final aim is to index texts with the countries and cities they mention and to automatically visualise this information on geographical maps using various tools.
Search
Co-authors