Correction Annotation for Non-Native Arabic Texts: Guidelines and Corpus

Zaghouani, Wajdi; Habash, Nizar; Bouamor, Houda; Rozovskaya, Alla; Mohit, Behrang; Heider, Abeer; Oflazer, Kemal

doi:10.3115/v1/w15-1614

Cited by 24 publications

(30 citation statements)

References 13 publications

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“…The objective of the model is to minimize the error of the network output versus the true sentiment class label for each training case. The remaining settings for DNN model are: (1) conjugate gradient algorithm is used for gradient updates with three line searches; (2) weights are randomly initialized from Gaussian distribution of 0 mean and standard deviation of 1; and (3) the activation function of each neuron is taken as hyperbolic tangent activation. Training is conducted in batches of size 100 cases for 50 epochs.…”

Section: Deep Learning Models For Sentiment Analysis In Arabicmentioning

confidence: 99%

“…2 Table 1 presents a sample Arabic news comment along with its manually corrected form, its romanized transliteration, 3 and the English translation. The errors in the original and the corrected forms are underlined and co-indexed.…”

Section: The Qalb Corpusmentioning

confidence: 99%

“…Further, while native texts mostly contain orthographic and punctuation mistakes, non-native writings also reveal lexical choice errors, missing and extraneous words (e.g. articles, prepositions), and mistakes in word 2 In the shared task, we specified two Add categories: add_before and add_after. Most of the add errors fall into the first category, and we combine these here into a single Add category.…”

Section: The Qalb Corpusmentioning

confidence: 99%

“…Most of the add errors fall into the first category, and we combine these here into a single Add category. 3 Arabic transliteration is presented in the Habash-SoudiBuckwalter scheme : (in alphabetical order) AbtθjHxdðrzsšSDTĎςγfqklmnhwy and the additional symbols: ' , Â ,Ǎ ,Ā ,ŵ ,ŷ ,h , ý . 4 Tables 1 and 2, and the appendix are reproduced from to help explain the format of the files used in QALB-2014 andQALB-2015 You cannot imagine the extent of my happiness when I read these wonderful and respectful analyses because I am a young man and I wish from God to perform Umrah passing through the Al-Aqsa Mosque; and it seemed that this was elusive that when anyone heard the wish, he would say that you can wish that your great grandchildren may achieve it because your wish is impossible.…”

Section: The Qalb Corpusmentioning

confidence: 99%

“…The CODA convention is presented and its goals and 2 We refer to (Guella, 2011) principals are described in details in (Habash et al, 2012a). An example of Algerian CODA is presented in Table 5.…”

Section: Algerian Arabic Coda Guidelinesmentioning

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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Section: Deep Learning Models For Sentiment Analysis In Arabicmentioning

confidence: 99%

Section: The Qalb Corpusmentioning

confidence: 99%

Section: The Qalb Corpusmentioning

confidence: 99%

Section: The Qalb Corpusmentioning

confidence: 99%

Section: Algerian Arabic Coda Guidelinesmentioning

confidence: 99%

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