TECHLIMED system description for the Shared Task on Automatic Arabic Error Correction

Mostefa, Djamel; Asbayou, Omar; Abbès, Ramzi

doi:10.3115/v1/w14-3621

Cited by 5 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7 ATB Broadcast news: Scripted, formal MSA as well as extemporaneous dialogue. 8 We extend our corpus and include texts covering various topics beyond the commonly-used news topics: classical Arabic books).…”

Section: Corpus Descriptionmentioning

confidence: 99%

Proceedings of the Second Workshop on Arabic Natural Language Processing

Ali¹,

Magdy²,

Renals³

2015

View full text Add to dashboard Cite

ii Foreword Assalamu 3alaykum wa nín hǎo! Welcome to the Second Arabic Natural Language Processing Workshop held at ACL 2015 in Beijing, China.A number of Arabic NLP (or Arabic NLP-related) workshops and conferences have taken place, both in the Arab World and in association with international conferences. The Arabic NLP workshop at ACL 2015 follows in the footsteps of these previous efforts to provide a forum for researchers to share and discuss their ongoing work. As in the first Arabic NLP workshop held at EMNLP 2014 in Doha, Qatar, this workshop includes a shared task on Automatic Arabic Error Correction, which was designed in the tradition of high profile NLP shared tasks such as CONLL's grammar/error detection and numerous machine translation campaigns by NIST/WMT/MEDAR, among others.We received 23 main workshop submissions and selected 15 (65%) for presentation in the workshop. Nine papers will be presented orally and six as part of a poster session. The presentation mode is independent of of the ranking of the papers. The papers cover a diverse set of topics from designing orthography conventions and annotation tools to speech recognition and deep learning for sentiment analysis.The shared task was a success with eight teams from six countries participating. The shared task system descriptions (short) papers are included in the proceedings to document the shared task systems, but were not reviewed with the rest of the papers of the main workshop. These papers will be presented as posters. A long paper describing the shared task will be presented orally.The quantity and quality of the contributions to the main workshop, as well as the shared task, are strong indicators that there is a continued need for this kind of dedicated Arabic NLP workshop.We would like to acknowledge all the hard work of the submitting authors and thank the reviewers for their diligent work and for the valuable feedback they provided. We are also thankful to the work of the shared task committee, website committee and the publication co-chairs. It has been an honor to serve as program co-chairs. We hope that the reader of these proceedings will find them stimulating and beneficial. AbstractDifferent names may be popular in different countries. Hence, person names may give a clue to a person's country of origin. Along with other features, mapping names to countries can be helpful in a variety of applications such as country tagging twitter users. This paper describes the collection of Arabic Twitter user names that are either written in Arabic or transliterated into Latin characters along with their stated geographical locations. To classify previously unseen names, we trained naive Bayes and Support Vector Machine (SVM) multi-class classifiers using primarily bag-of-words features. We are able to map Arabic user names to specific Arab countries with 79% accuracy and to specific regions (Gulf, Egypt, Levant, Maghreb, and others) with 94% accuracy. As for transliterated Arabic names, the accuracy per country and per region was 67...

show abstract

Section: Corpus Descriptionmentioning

confidence: 99%

Proceedings of the Second Workshop on Arabic Natural Language Processing

Ali¹,

Magdy²,

Renals³

2015

View full text Add to dashboard Cite

show abstract

“…Another very popular way of writing Arabic on the Internet and the social media like Facebook or Tweeter is to use "Arabizi", a Latinized form of writing Arabic using Latin letters and digits (Aboelezz 2009). For our participation in this second QALB Shared Task, we tried to improve the systems we have developed for the first edition (Mostefa 2014). The first approach is a lexicon driven spell checker using Hunspell (Hunspell 2007).…”

Section: Introductionmentioning

confidence: 99%

TECHLIMED$@$QALB-Shared Task 2015: a hybrid Arabic Error Correction System

Mostefa¹,

Abualasal²,

Asbayou³

et al. 2015

Proceedings of the Second Workshop on Arabic Natural Language Processing

Self Cite

View full text Add to dashboard Cite

This paper reports on the participation of Techlimed in the Second Shared Task on Automatic Arabic Error Correction organized by the Arabic Natural Language Processing Workshop. This year's competition includes two tracks, and, in addition to errors produced by native speakers (L1), also includes correction of texts written by learners of Arabic as a foreign language (L2). Techlimed participated in the L1 track. For our participation in the L1 evaluation task, we developed two systems. The first one is based on the spellchecker Hunspell with specific dictionaries. The second one is a hybrid system based on rules, morphology analysis and statistical machine translation. Our results on the test set show that the hybrid system outperforms the lexicon driven approach with a precision of 71.2%, a recall of 64.94% and an F-measure of 67.93%.

show abstract

“…Team Name Affiliation CLMB Columbia University (USA) CMUQ (Jeblee et al, 2014) Carnegie Mellon University in Qatar (Qatar) CP13 Université Paris 13 (France) CUFE (Nawar and Ragheb, 2014) Computer Engineering Department, Cairo University (Egypt) GLTW (Zerrouki et al, 2014) Bouira University (Algeria), The National Computer Science Engineering School (Algeria), and Tabuk University (KSA) GWU (Attia et al, 2014) George Washington University (USA) QCRI (Mubarak and Darwish, 2014) Qatar Computing Research Institute (Qatar) TECH (Mostefa et al, 2014) Techlimed.com (France) YAM (Hassan et al, 2014) Faculty of Engineering, Cairo University (Egypt) Table 6: Approaches adopted by the participating teams.…”

Section: Shared Task Datamentioning

confidence: 99%

The First QALB Shared Task on Automatic Text Correction for Arabic

Mohit

Rozovskaya

Habash

et al. 2014

Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)

View full text Add to dashboard Cite

We present a summary of the first shared task on automatic text correction for Arabic text. The shared task received 18 systems submissions from nine teams in six countries and represented a diversity of approaches. Our report includes an overview of the QALB corpus which was the source of the datasets used for training and evaluation, an overview of participating systems, results of the competition and an analysis of the results and systems.

show abstract

TECHLIMED system description for the Shared Task on Automatic Arabic Error Correction

Cited by 5 publications

References 6 publications

Proceedings of the Second Workshop on Arabic Natural Language Processing

Proceedings of the Second Workshop on Arabic Natural Language Processing

TECHLIMED$@$QALB-Shared Task 2015: a hybrid Arabic Error Correction System

The First QALB Shared Task on Automatic Text Correction for Arabic

Contact Info

Product

Resources

About