Towards Arabic Spell-Checker Based on N-Grams Scores

Muaidi, Hasan; Al-tarawneh, Rasha

doi:10.5120/8400-2168

Cited by 9 publications

(10 citation statements)

References 7 publications

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“…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%

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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Section: Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Arabic Natural Language Processing

Ali¹,

Magdy²,

Renals³

2015

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“…In [1], Muaidi et al divide each word into bigrams to develop an Arabic spell-checker. Each bigram is given a score.…”

Section: Arabic Normalizationmentioning

confidence: 99%

“…Each tweet is limited to 140 characters 1 and can comprise text, links, symbols, videos, and, or pictures. Users in Twitter may have both a following and followers, thereby forming a social network.…”

Section: Introductionmentioning

confidence: 99%

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Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Abozinadah¹,

Jones²

2016

IJDKP

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“…The corpus consists of 101,987 word types. It is implemented by a visual basic tool that browse daily newspapers and articles via web site [6].…”

Section: Corpus Creationmentioning

confidence: 99%

Spelling Detection Errors Techniques in NLP: A Survey

Al-tarawneh¹

2017

IJCA

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This paper reports the efforts of Arabic Spell-checker researches by providing a brief summary of proposed methods and techniques that explains how the spelling errors might be discovered in any entered text. It mainly unites two areas that are quite different in appearance, computer science and natural languages .The domain of this topic is limited because of the complex morphology it has compared with other Languages, and the variation of its stems and the similarity of the characters for this language. This poses a challenge for the researchers to concern about it.

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Towards Arabic Spell-Checker Based on N-Grams Scores

Cited by 9 publications

References 7 publications

Proceedings of the Second Workshop on Arabic Natural Language Processing

Proceedings of the Second Workshop on Arabic Natural Language Processing

Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Spelling Detection Errors Techniques in NLP: A Survey

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