Automatic Correction of Arabic Text: a Cascaded Approach

Mubarak, Hamdy; Darwish, Kareem

doi:10.3115/v1/w14-3617

Cited by 15 publications

(16 citation statements)

References 7 publications

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“…In The companion volume of the proceedings of international joint conference on natural language processing (ijcnlp) (pp. [13][14][15][16]. …”

Section: Referencesmentioning

confidence: 99%

“…Techniques and tools reported in the literature for supporting the Arabic spelling errors detection and correction task include morphological analysis [12] [16], finite state transducer with edit distance [9] [8], statistical character level transformation [14], N-gram scores [17] [8], conditional random fields [14] [8], and Naïve Base [15]. Similar to systems described in the literature, Arib utilizes language resources such as dictionaries and corpora as well as the application of different techniques to support the task of Arabic spelling error detection and correction.…”

Section: Related Workmentioning

confidence: 99%

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Proceedings of the Second Workshop on Arabic Natural Language Processing

Ali¹,

Magdy²,

Renals³

2015

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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...

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“…In The companion volume of the proceedings of international joint conference on natural language processing (ijcnlp) (pp. [13][14][15][16]. …”

Section: Referencesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Arabic Natural Language Processing

Ali¹,

Magdy²,

Renals³

2015

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“…Significant contributions were also introduced in the 2014 Shared Task on Arabic Error Correction including Nawar and Ragheb, 2014;Jeblee et al, 2014;and Mubarak and Darwish, 2014).…”

Section: Introductionmentioning

confidence: 99%

GWU-HASP-2015$@$QALB-2015 Shared Task: Priming Spelling Candidates with Probability

Attia

Al-Badrashiny

Diab

2015

Proceedings of the Second Workshop on Arabic Natural Language Processing

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In this paper, we describe our system HASP-2015 (Hybrid Arabic Spelling and Punctuation Corrector) in which we introduce significant improvements over our previous version HASP-2014 and with which we participated in the QALB-2015 Second Shared Task on Arabic Error Correction. Our system utilizes probabilistic information on errors and their possible corrections in the training data and combine that with an open-source reference dictionary (or word list) for detecting errors and generating and filtering candidates. We enhance our system further by allowing it to generate candidates for common semantic and grammatical errors. Eventually, an n-gram language model is used for selecting best candidates. We use a CRF (Conditional Random Fields) classifier for correcting punctuation errors in a two-pass process where first the system learns punctuation placement, and then it learns to identify punctuation types.

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“…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)

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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.

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Automatic Correction of Arabic Text: a Cascaded Approach

Cited by 15 publications

References 7 publications

Proceedings of the Second Workshop on Arabic Natural Language Processing

Proceedings of the Second Workshop on Arabic Natural Language Processing

GWU-HASP-2015$@$QALB-2015 Shared Task: Priming Spelling Candidates with Probability

The First QALB Shared Task on Automatic Text Correction for Arabic

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