Elamur Research⌕
☰
home / papers / 2019 / Arabic natural language processing: An overview

Arabic natural language processing: An overview

Summary

Arabic NLP is fragmented. The language has three broad varieties: Classical Arabic (CA, e.g. the Quran), Modern Standard Arabic (MSA), and many Arabic dialects (AD). MSA and the dialects are also often typed in Latin letters and digits, a practice called Arabizi. Earlier surveys ended around 2015, largely skipped CA and Arabizi, and seldom pointed readers to tools they could actually use. This overview reviews 90 studies, most from 2015–2018. It sorts them by variety (CA, MSA, AD, Arabizi) and by research area, reusing an older four-way scheme: basic language analyses (BLA), building resources (BR), language identification (LI), and semantic-level analysis such as machine translation and sentiment analysis. Transliteration is added as an extra area for Arabizi. For each study the authors note whether its data or software is freely obtainable. Their findings: CA is barely studied, Arabizi work is recent and its resources are mostly closed, dialect coverage is uneven, and most resources are built by hand. The paper is useful as a map of existing resources and as a list of open methodological questions.

Key points

  • 90 studies are covered. 67 of them (74%) appeared in 2015–2018, and 52 (58%) are linked to a freely available tool or resource. The discussion section gives a slightly different figure: 50/90 (55.6%) provide resources.
  • BLA is the largest area, with 24/90 works (26.7%). The authors attribute this to Arabic's rich morphology, which makes POS tagging, stemming and lemmatisation necessary first steps.
  • Dialects account for 40 works, 17 of them multi-dialect. Of the 23 single-dialect works:
    • Saudi/Gulf: 7
    • Egyptian: 6
    • Algerian: 4
    • Tunisian and Moroccan: 2 each
    • Palestinian and Levantine: 1 each
  • For Algerian, PADIC is the only public resource.
  • For CA, the Quranic Arabic Corpus (Dukes et al., 2010) combines three layers: morphological annotation, a syntactic treebank with dependency graphs labelled using traditional Arabic grammar relations, and a semantic ontology.
  • Arabizi example: "rani fer7ana" means "I am happy".
    • Recent Arabizi work treats it per dialect (Algerian, Egyptian) rather than as one language.
    • No study tells apart Algerian, Tunisian or Egyptian Arabizi; identification work only separates Arabizi from French and English.

Interesting ideas

  • I1. Catalogue every surveyed work together with a pointer to its public resource and the resource type, so the review doubles as an inventory of reusable assets rather than just a reading list. — For Mova (learn): Keep a similar registry of every treebank, lexicon and silver set Mova trains on, recording availability and provenance, so each model's inputs stay explainable.
  • I2. The Quranic Arabic Corpus places morphology, a dependency treebank and a semantic ontology on the same text, letting each layer be checked against the others. — For Mova (world): Build small multi-layer gold sets where tokens, UD tree, frames and world entities are aligned on one passage, giving a direct test of how errors propagate between stages.
  • I3. Treat the writing system (Arabic script vs Latin Arabizi) as separate from the language variety, since one script can carry several dialects and one dialect can appear in two scripts. — For Mova (tokens): Tag input with orthographic register (standard spelling, chat spelling, digits-as-letters) independently of language, so the tokenizer and morphology can normalise before tagging.
  • I4. The authors ask whether transliteration really has to come before semantic analysis, and whether a normalisation step is required at all. — For Mova (tree): Run an ablation comparing parsing of normalised vs raw noisy text, and check whether the UD trees and downstream frames actually differ.
  • I5. They plan to judge resources extrinsically, by applying each one to the same external data rather than trusting its own reported scores. — For Mova (answer): Rate each compiled knowledge source by its effect on a fixed set of school tests and fable questions, instead of by its intrinsic size or accuracy.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

47 papers of our library cite it (of the 140884 whose bibliographies we read); 11 of them have a page here, 36 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1016/j.jksuci.2019.02.006 · arXiv 1903.02784

@misc{guellil2019arabic,
  title = {{Arabic natural language processing: An overview}},
  author = {Guellil, Imane and Saadane, Houda and Azouaou, Faical and Gueni, Billel and Nouvel, Damien},
  year = {2019},
  doi = {10.1016/j.jksuci.2019.02.006},
  eprint = {1903.02784},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1016/j.jksuci.2019.02.006}
}

References

118 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Abainia, K., Ouamour, S., Sayoud, H., 2017. A novel robust arabic light stemmer. J. Exp. Theor. Artif. Intell. 29, 557–573. find
  2. Abdelali, A., Darwish, K., Durrani, N., Mubarak, H., 2016. Farasa: a fast and furious segmenter for arabic. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 11–16. find our card
  3. Abdul-Mageed, M., Alhuzali, H., Elaraby, M., 2018. You tweet what you speak: a city-level dataset of arabic dialects. LREC. find
  4. Abdul-Mageed, M., Diab, M.T., 2012. Awatif: A Multi-genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis. LREC, Citeseer, pp. 3907– 3914. find
  5. Abdul-Mageed, M., Diab, M.T., 2016. Sana: A Large Scale Multi-genre, Multi-dialect Lexicon for Arabic Subjectivity and Sentiment Analysis. LREC. find
  6. Abidi, K., Menacer, M.A., Smaili, K., 2017. Calyou: A comparable spoken algerian corpus harvested from youtube. In: 18th Annual Conference of the International Communication Association (Interspeech). find
  7. Adeleke, A.O., Samsudin, N.A., Mustapha, A., Nawi, N.M., 2017. Comparative analysis of text classification algorithms for automated labelling of quranic verses. Int. J. Adv. Sci. Eng. Info. Tech. 7, 1419–1427. find
  8. Al-Badrashiny, M., Eskander, R., Habash, N., Rambow, O., 2014. Automatic transliteration of romanized dialectal arabic. In: Proceedings of the Please cite this article as: I. Guellil, H. Saâdane, F. Azouaou et al., Arabic natural language processing: An overview, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.006 Eighteenth Conference on Computational Natural Language Learning, pp. 30– 38. ↗ our card
  9. Al-Kabi, M.N., Alsmadi, I.M., Wahsheh, H.A., Abu Ata, B.M., 2013. A topical classification of quranic arabic text, NOORIC 2013: Taibah University International Conference on Advances in Information Technology for the Holy Quran and Its Sciences. find
  10. Al-Shargi, F., Kaplan, A., Eskander, R., Habash, N., Rambow, O., 2016. Morphologically annotated corpora and morphological analyzers for moroccan and sanaani yemeni arabic. In: 10th Language Resources and Evaluation Conference (LREC 2016). find
  11. Al-Shargi, F., Rambow, O., 2015. Diwan: A dialectal word annotation tool for arabic. In: Proceedings of the Second Workshop on Arabic Natural Language Processing, pp. 49–58. find
  12. Al-Twairesh, N., Al-Khalifa, H., Al-Salman, A., Al-Ohali, Y., 2017. Arasenti-tweet: a corpus for arabic sentiment analysis of saudi tweets. Proc. Comput. Sci. 117, 63– 72. find
  13. Al-Twairesh, N., Al-Khalifa, H., AlSalman, A., 2016. Arasenti: large-scale twitter-specific arabic sentiment lexicons. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (volume 1: Long Papers), pp. 697–705. find
  14. Al-Twairesh, N., Al-Matham, R., Madi, N., Almugren, N., Al-Aljmi, A.H., Alshalan, S., Alshalan, R., Alrumayyan, N., Al-Manea, S., Bawazeer, S., et al., 2018. Suar: Towards building a corpus for the Saudi dialect. Proc. Comput. Sci. 142, 72–82. find
  15. Alharbi, R., Magdy, W., Darwish, K., AbdelAli, A., Mubarak, H., 2018. Part-of-speech tagging for arabic gulf dialect using bi-lstm. find
  16. Ali, A., Dehak, N., Cardinal, P., Khurana, S., Yella, S.H., Glass, J., Bell, P., Renals, S., 2016. Automatic dialect detection in arabic broadcast speech. In: Interspeech, San Francisco, CA, USA, pp. 2934–2938. find
  17. Ali, A., Vogel, S., Renals, S., 2017. Speech recognition challenge in the wild: Arabic mgb-3. In: Automatic Speech Recognition and Understanding Workshop (ASRU), 2017 IEEE. IEEE, pp. 316–322. find our card
  18. Ali, M., 2018. Character level convolutional neural network for arabic dialect identification. In: Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018), pp. 122–127. find
  19. Almeman, K., Lee, M., 2013. Automatic building of arabic multi dialect text corpora by bootstrapping dialect words. In: Communications, signal processing, and their applications (iccspa), 2013 1st international conference on, Citeseer, pp. 1–6. find
  20. Alsarsour, I., Mohamed, E., Suwaileh, R., Elsayed, T., 2018. Dart: a large dataset of dialectal arabic tweets. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). find
  21. Aly, M., Atiya, A., 2013. Labr: a large scale arabic book reviews dataset. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 494–498 (Volume 2: Short Papers). find
  22. Asda, T.M.H., Gunawan, T.S., Kartiwi, M., Mansor, H., 2016. Development of quran reciter identification system using mfcc and neural network. find
  23. Azouaou, F., Guellil, I., 2017. Alg/fr: A step by step construction of a lexicon between algerian dialect and french, in: The 31st Pacific Asia Conference on Language, Information and Computation PACLIC 31 (2017). find
  24. Baccianella, S., Esuli, A., Sebastiani, F., 2010. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. LREC. find our card
  25. Badaro, G., Baly, R., Hajj, H., Habash, N., El-Hajj, W., 2014. A large scale arabic sentiment lexicon for arabic opinion mining. In: Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pp. 165–173. find
  26. Badaro, G., El Jundi, O., Khaddaj, A., Maarouf, A., Kain, R., Hajj, H., El-Hajj, W., 2018. Ema at semeval-2018 task 1: Emotion mining for arabic. In: Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 236–244. find
  27. Bahdanau, D., Cho, K., Bengio, Y., 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. ↗ our card
  28. Belinkov, Y., Magidow, A., Romanov, M., Shmidman, A., Koppel, M., 2016. Shamela: a large-scale historical arabic corpus. arXiv preprint arXiv:1612.08989. ↗
  29. Bies, A., Song, Z., Maamouri, M., Grimes, S., Lee, H., Wright, J., Strassel, S., Habash, N., Eskander, R., Rambow, O., 2014. Transliteration of arabizi into arabic orthography: developing a parallel annotated arabizi-arabic script sms/chat corpus. In: Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pp. 93–103. find
  30. Bouamor, H., Habash, N., Salameh, M., Zaghouani, W., Rambow, O., Abdulrahim, D., Obeid, O., Khalifa, S., Eryani, F., Erdmann, A., et al., 2018. The madar arabic dialect corpus and lexicon. LREC. find
  31. Boudad, N., Faizi, R., Thami, R.O.H., Chiheb, R., 2017. Sentiment analysis in arabic: a review of the literature. Ain Shams Eng. find
  32. J. Chang, C.C., Lin, C.J., 2011. Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27. find
  33. Dahou, A., Xiong, S., Zhou, J., Haddoud, M.H., Duan, P., 2016. Word embeddings and convolutional neural network for arabic sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2418–2427. find
  34. Darwish, K., 2014. Arabizi detection and conversion to arabic. In: Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pp. 217–224. find
  35. Darwish, K., Mubarak, H., Abdelali, A., Eldesouki, M., Samih, Y., Alharbi, R., Attia, M., Magdy, W., Kallmeyer, L., 2018. Multi-dialect Arabic Pos Tagging: A CRF Approach. LREC. find
  36. Dima, T., Jamila, E.G., Nizar, H., 2018. An arabic dependency treebank in the travel domain. In: 11th Language Resources and Evaluation Conference. find our card
  37. Dukes, K., Atwell, E., Sharaf, A.B.M., 2010. Syntactic Annotation Guidelines for the Quranic Arabic Dependency Treebank. LREC. find our card
  38. Duwairi, R.M., Alfaqeh, M., Wardat, M., Alrabadi, A., 2016. Sentiment analysis for arabizi text, in: Information and Communication Systems (ICICS). 2016 7th International Conference on, IEEE. pp. 127–132. find
  39. El-Beltagy, S.R., 2016. Nileulex: A Phrase and Word Level Sentiment Lexicon for Egyptian and Modern Standard Arabic. LREC. find
  40. El-Haj, M., Koulali, R., 2013. Kalimat a multipurpose arabic corpus. In: Second Workshop on Arabic Corpus Linguistics (WACL-2), pp. 22–25. find
  41. El Haj, M., Rayson, P.E., Aboelezz, M., 2017. Arabic dialect identification in the context of bivalency and code-switching. find
  42. Erdmann, A., Zalmout, N., Habash, N., 2018. Addressing noise in multidialectal word embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 558–565 (Volume 2: Short Papers). find
  43. Eskander, R., Al-Badrashiny, M., Habash, N., Rambow, O., 2014. Foreign words and the automatic processing of arabic social media text written in roman script. Proceedings of The First Workshop on Computational Approaches to Code Switching, 1–12. find
  44. Eskander, R., Rambow, O., 2015. Slsa: A sentiment lexicon for standard arabic. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2545–2550. find
  45. Esuli, A., Sebastiani, F., 2007. Sentiwordnet: a high-coverage lexical resource for opinion mining. Evaluation 17, 1–26. find
  46. Farghaly, A., Shaalan, K., 2009. Arabic natural language processing: challenges and solutions. ACM Transactions on Asian Language Information Processing (TALIP) 8, 14. find
  47. Graves, A., Fernández, S., Gomez, F., Schmidhuber, J., 2006. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on Machine learning ACM, pp. 369–376. find
  48. Guellil, I., Adeel, A., Azouaou, F., Benali, F., Hachani, A.E., Hussain, A., 2018. Arabizi sentiment analysis based on transliteration and automatic corpus annotation. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 335–341. find
  49. Guellil, I., Adeel, A., Azouaou, F., Hussain, A., 2018b. Sentialg: Automated corpus annotation for algerian sentiment analysis. In: 9th International Conference on Brain Inspired Cognitive Systems(BICS 2018). find
  50. Guellil, I., Azouaou, F., 2016. Arabic dialect identification with an unsupervised learning (based on a lexicon). application case: Algerian dialect. In: Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), 2016 IEEE Intl Conference on, IEEE. pp. 724–731. find
  51. Guellil, I., Azouaou, F., 2016. Asda: Analyseur syntaxique du dialecte algérien dans un but d’analyse sémantique. In: Conférence Nationale d’Intelligence Artificielle Année 2016, Association Française pour l’Intelligence Artificielle AFIA. afia.asso. fr/wp. pp. 87–94. find
  52. Guellil, I., Azouaou, F., Abbas, M., 2017. Comparison between neural and sta-tistical translation after transliteration of algerian arabic dialect. In: WiNLP: Women & Underrepresented Minorities in Natural Language Processing (co-located withACL 2017). find
  53. Guellil, I., Azouaou, F., Abbas, M., 2017. Neural vs statistical translation of algerian arabic dialect written with arabizi and arabic letter. In: The 31st Pacific Asia Conference on Language, Information and Computation PACLIC 31 (2017). find
  54. Guellil, I., Azouaou, F., Abbas, M., Fatiha, S., 2017. Arabizi transliteration of algerian arabic dialect into modern standard arabic. In: Social MT 2017: First workshop on Social Media and User Generated Content Machine Translation (co-located with EAMT 2017). find
  55. Guellil, I., Azouaou, F., Benali, F., Hachani, A.E., Saadane, H., 2018. Approche hybride pour la translitération de l’arabizi algérien: une étude préliminaire. In: Conference: 25e conférence sur le Traitement Automatique des Langues Naturelles (TALN), May 2018, Rennes, FranceAt: Rennes, France,https://www.researchgate.net/publication. ↗
  56. Guellil, I., Azouaou, F., Saâdane, H., Semmar, N., 2017. Une approche fondée sur les lexiques d’analyse de sentiments du dialecte algérien. find
  57. Guellil, I., Faical, A., 2017. Bilingual lexicon for algerian arabic dialect treatment in social media. In: WiNLP: Women & Underrepresented Minorities in Natural Language Processing (co-located with ACL 2017).http://www.winlp.org/wp-content/uploads/2017/final_papers_2017/92_Paper.pdf. ↗
  58. Habash, N., Eryani, F., Khalifa, S., Rambow, O., Abdulrahim, D., Erdmann, A., Faraj, R., Zaghouani, W., Bouamor, H., Zalmout, N., et al., 2018. Unified Guidelines and Resources for Arabic Dialect Orthography. LREC. find our card
  59. Habash, N., Eskander, R., Hawwari, A., 2012. A morphological analyzer for egyptian arabic. In: Proceedings of the twelfth meeting of the special interest group on computational morphology and phonology. Association for Computational Linguistics, pp. 1–9. find our card
  60. Habash, N., Soudi, A., Buckwalter, T., 2007. On arabic transliteration. In: Arabic Computational Morphology. Springer, pp. 15–22. find
  61. Habash, N.Y., 2010. Introduction to arabic natural language processing. Synthesis Lectures on Human Language Technologies 3, 1–187. find
  62. Harrat, S., Meftouh, K., Smaïli, K., 2017. Maghrebi arabic dialect processing: an overview. In: ICNLSSP 2017-International Conference on Natural Language, Signal and Speech Processing. find
  63. Inoue, G., Habash, N., Matsumoto, Y., Aoyama, H., 2018. A Parallel Corpus of Arabic-japanese News Articles. LREC. Please cite this article as: I. Guellil, H. Saâdane, F. Azouaou et al., Arabic natural language processing: An overview, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.006 ↗ our card
  64. Jarrar, M., Habash, N., Alrimawi, F., Akra, D., Zalmout, N., 2017. Curras: an annotated corpus for the palestinian arabic dialect. Lang. Resour. Eval. 51, 745–775. find
  65. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T., 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759. ↗
  66. Khalifa, S., Habash, N., Abdulrahim, D., Hassan, S., 2016. A large scale corpus of gulf arabic. arXiv preprint arXiv:1609.02960. ↗
  67. Khalifa, S., Habash, N., Eryani, F., Obeid, O., Abdulrahim, D., Al Kaabi, M., 2018. A morphologically annotated corpus of emirati arabic. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). find
  68. Khalifa, S., Hassan, S., Habash, N., 2017. A morphological analyzer for gulf arabic verbs. In: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 35–45. find
  69. Khalifa, S., Zalmout, N., Habash, N., 2016b. Yamama: Yet another multi-dialect arabic morphological analyzer. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pp. 223–227. find
  70. Kim, Y., Jernite, Y., Sontag, D., Rush, A.M., 2016. Character-aware neural language models. find our card
  71. Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., et al., 2007. Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions. Association for Computational Linguistics, pp. 177–180. find
  72. Kumar, G., Cao, Y., Cotterell, R., Callison-Burch, C., Povey, D., Khudanpur, S., 2014. Translations of the Callhome Egyptian Arabic Corpus for Conversational Speech Translation. IWSLT, Citeseer. find
  73. Kwaik, K.A., Saad, M., Chatzikyriakidis, S., Dobnik, S., 2018. Shami: A corpus of levantine arabic dialects. find
  74. Le, Q., Mikolov, T., 2014. Distributed representations of sentences and documents. International Conference on Machine Learning, 1188–1196. find our card
  75. Lison, P., Tiedemann, J., 2016. Opensubtitles 2016: Extracting large parallel corpora from movie and tv subtitles. find
  76. Maamouri, M., Bies, A., Buckwalter, T., Mekki, W., 2004. The penn arabic treebank: building a large-scale annotated arabic corpus. In: NEMLAR Conference on Arabic Language Resources and Tools, Cairo, pp. 466–467. find
  77. Maamouri, M., Bies, A., Kulick, S., Ciul, M., Habash, N., Eskander, R., 2014. Developing an egyptian arabic treebank: impact of dialectal morphology on annotation and tool development. LREC. find
  78. May, J., Benjira, Y., Echihabi, A., 2014. An arabizi-english social media statistical machine translation system. In: Proceedings of the 11th Conference of the Association for Machine Translation in the Americas, pp. 329–341. find
  79. Medhaffar, S., Bougares, F., Esteve, Y., Hadrich-Belguith, L., 2017. Sentiment analysis of tunisian dialects: Linguistic resources and experiments. In: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 55–61. find
  80. Meftouh, K., Harrat, S., Smaïli, K., 2018. Padic: extension and new experiments. In: 7th International Conference on Advanced Technologies. ICAT. find
  81. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., 2013. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Processing Systems, 3111–3119. find
  82. Mohammad, S., Salameh, M., Kiritchenko, S., 2016. Sentiment Lexicons for Arabic Social Media. LREC. find
  83. Monroe, W., Green, S., Manning, C.D., 2014. Word segmentation of informal arabic with domain adaptation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 206–211 (Volume 2: Short Papers). find
  84. More, A., Çetinoğlu, Ö., Çöltekin, Ç., Habash, N., Sagot, B., Seddah, D., Taji, D., Tsarfaty, R., 2018. Conll-ul: Universal morphological lattices for universal dependency parsing. In: 11th Language Resources and Evaluation Conference. find
  85. Obeid, O., Khalifa, S., Habash, N., Bouamor, H., Zaghouani, W., Oflazer, K., 2018. Madari: A web interface for joint arabic morphological annotation and spelling correction. arXiv preprint arXiv:1808.08392. ↗
  86. Och, F.J., Ney, H., 2003. A systematic comparison of various statistical alignment models. Comput. Ling. 29, 19–51. find
  87. Pasha, A., Al-Badrashiny, M., Diab, M.T., El Kholy, A., Eskander, R., Habash, N., Pooleery, M., Rambow, O., Roth, R., 2014. Madamira: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic. LREC, pp. 1094– 1101. find our card
  88. Rosca, M., Breuel, T., 2016. Sequence-to-sequence neural network models for transliteration. arXiv preprint arXiv:1610.09565. ↗
  89. Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M., 2011. Oca: opinion corpus for arabic. J. Assoc. Inf. Sci. Technol. 62, 2045–2054. find
  90. Saad, M., Alijla, B.O., 2017. Wikidocsaligner: an off-the-shelf wikipedia documents alignment tool. In: Information and Communication Technology (PICICT), 2017 Palestinian International Conference on. IEEE, pp. 34–39. find
  91. Saadane, H., Habash, N., 2015. A conventional orthography for algerian arabic. In: ANLP Workshop 2015, p. 69. find
  92. Sadat, F., Mallek, F., Boudabous, M., Sellami, R., Farzindar, A., 2014. Collaboratively constructed linguistic resources for language variants and their exploitation in NLP application – the case of tunisian arabic and the social media. Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing, 102–110. find
  93. Salameh, M., Bouamor, H., 2018. Fine-grained arabic dialect identification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1332–1344. find our card
  94. Salloum, W., Habash, N., 2014. Adam: analyzer for dialectal arabic morphology. J. King Saud Univ.-Comput. Inf. Sci. 26, 372–378. find
  95. Samih, Y., Attia, M., Eldesouki, M., Abdelali, A., Mubarak, H., Kallmeyer, L., Darwish, K., 2017. A neural architecture for dialectal arabic segmentation. In: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 46–54. find our card
  96. Selab, E., Guessoum, A., 2015. Building talaa, a free general and categorized arabic corpus. in: ICAART (1), pp. 284–291. find
  97. Shahrour, A., Khalifa, S., Habash, N., 2015. Improving arabic diacritization through syntactic analysis. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1309–1315. find our card
  98. Shahrour, A., Khalifa, S., Taji, D., Habash, N., 2016. Camelparser: a system for arabic syntactic analysis and morphological disambiguation. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pp. 228–232. find our card
  99. Sharaf, A.B.M., Atwell, E., 2012a. Qurana: Corpus of the Quran Annotated with Pronominal Anaphora. LREC, Citeseer. find
  100. Sharaf, A.B.M., Atwell, E., 2012b. Qursim: A Corpus for Evaluation of Relatedness in Short Texts. LREC, Citeseer. find
  101. Shon, S., Ali, A., Glass, J., 2018. Convolutional neural networks and language embeddings for end-to-end dialect recognition. arXiv preprint arXiv:1803.04567. ↗
  102. Shoufan, A., Alameri, S., 2015. Natural language processing for dialectical arabic: a survey. In: Proceedings of the Second Workshop on Arabic Natural Language Processing, pp. 36–48. find
  103. Soliman, A.B., Eissa, K., El-Beltagy, S.R., 2017. Aravec: A set of arabic word embedding models for use in arabic NLP. Proc. Comput. Sci. 117, 256–265. find
  104. Stolcke, A., 2002. Srilm-an extensible language modeling toolkit. In: Seventh International Conference on Spoken Language processing. find
  105. Suwaileh, R., Kutlu, M., Fathima, N., Elsayed, T., Lease, M., 2016. Arabicweb16: A new crawl for today’s arabic web. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval ACM, pp. 673–676. find
  106. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M., 2011. Lexicon-based methods for sentiment analysis. Comput. Linguistics 37, 267–307. find our card
  107. Tachicart, R., Bouzoubaa, K., Aouragh, S.L., Jaafa, H., 2017. Automatic identification of moroccan colloquial arabic. In: International Conference on Arabic Language Processing. Springer, pp. 201–214. find
  108. Tachicart, R., Bouzoubaa, K., Jaafar, H., 2014. Building a moroccan dialect electronic dictionary (MDED). In: 5th International Conference on Arabic Language Processing, pp. 216–221. find
  109. Taji, D., Habash, N., Zeman, D., 2017. Universal dependencies for arabic. In: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 166–176. find our card
  110. Takezawa, T., 2006. Multilingual spoken language corpus development for communication research. In: Chinese Spoken Language Processing. Springer, pp. 781–791. find
  111. Tiedemann, J., 2007. Improved sentence alignment for movie subtitles. In: Proceedings of RANLP. van der Wees, M., Bisazza, A., Monz, C., 2016. A simple but effective approach to improve arabizi-to-english statistical machine translation. In: Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pp. 43–50. find
  112. Yousfi, S., Berrani, S.A., Garcia, C., 2015. Alif: A dataset for arabic embedded text recognition in tv broadcast. In: Document Analysis and Recognition (ICDAR), 2015 13th International Conference on, IEEE. pp. 1221–1225. find
  113. Zaidan, O.F., Callison-Burch, C., 2014. Arabic dialect identification. Comput. Linguistics 40, 171–202. find our card
  114. Zalmout, N., Erdmann, A., Habash, N., 2018. Noise-robust morphological disambiguation for dialectal arabic. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 953–964. find our card
  115. Zalmout, N., Habash, N., 2017. Don’t throw those morphological analyzers away just yet: neural morphological disambiguation for arabic, in. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 704–713. find our card
  116. Zerrouki, T., Balla, A., 2017. Tashkeela: novel corpus of arabic vocalized texts, data for auto-diacritization systems. Data in Brief 11, 147. find
  117. Ziemski, M., Junczys-Dowmunt, M., Pouliquen, B., 2016. The United Nations Parallel Corpus v1. LREC. find
  118. Zribi, I., Khemakhem, M.E., Belguith, L.H., 2013. Morphological analysis of tunisian dialect. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 992–996. Please cite this article as: I. Guellil, H. Saâdane, F. Azouaou et al., Arabic natural language processing: An overview, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.006 ↗ our card

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/guellil-2019-arabic-natural-language-processing-overview/