Morphological disambiguation of Tunisian dialect

Zribi, Inès; Ellouze, Mariem; Belguith, Lamia Hadrich; Blache, Philippe

doi:10.1016/j.jksuci.2017.01.004

Cited by 23 publications

(15 citation statements)

References 10 publications

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“…Authors in [25] converted the ECAL (Egyptian Colloquial Arabic Lexicon) to SAMA (Standard Modern Arabic Analyser) representation [26]. For Tunisian dialect, authors in [27] adapted Al-Khalil MA, they create a lexicon by converting MSA patterns to Tunisian dialect patterns and then extracting specific roots and patterns from a training corpus that they created.…”

Section: A Related Workmentioning

confidence: 99%

An Algerian dialect: Study and Resources

Harrat¹,

Meftouh²,

Abbas³

et al. 2016

ijacsa

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Abstract-Arabic is the official language overall Arab countries, it is used for official speech, news-papers, public administration and school. In Parallel, for everyday communication, nonofficial talks, songs and movies, Arab people use their dialects which are inspired from Standard Arabic and differ from one Arabic country to another. These linguistic phenomenon is called disglossia, a situation in which two distinct varieties of a language are spoken within the same speech community. It is observed Throughout all Arab countries, standard Arabic widely written but not used in everyday conversation, dialect widely spoken in everyday life but almost never written. Thus, in NLP area, a lot of works have been dedicated for written Arabic. In contrast, Arabic dialects at a near time were not studied enough. Interest for them is recent. First work for these dialects began in the last decade for middle-east ones. Dialects of the Maghreb are just beginning to be studied. Compared to written Arabic, dialects are under-resourced languages which suffer from lack of NLP resources despite their large use. We deal in this paper with Arabic Algerian dialect a non-resourced language for which no known resource is available to date. We present a first linguistic study introducing its most important features and we describe the resources that we created from scratch for this dialect.

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Section: A Related Workmentioning

confidence: 99%

An Algerian dialect: Study and Resources

Harrat¹,

Meftouh²,

Abbas³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…The second one applies word segmentation and uses web data as a corpus to produce statistical information about the frequency of different segment combinations. In (Zribi, Khemakhem, & Belguith, 2013), a morphological analyzer for the Tunisian dialect based on a MSA analyzer was proposed. Furthermore, a lexicon for the Tunisian dialect is built as an expansion of a MSA lexicon.…”

Section: Morphological Analysis and Pos Taggingmentioning

confidence: 99%

“…The rules presented are based on the standard Arabic transcription conventions. This work was later used in (Zribi, Khemakhem, & Belguith, 2013) for morphological analysis presented in the Morphological Analysis and POS Tagging section.…”

Section: Orthographic Analysismentioning

confidence: 99%

Natural Language Processing for Dialectical Arabic: A Survey

Shoufan

Alameri

2015

Proceedings of the Second Workshop on Arabic Natural Language Processing

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“…Such resources and techniques, however, are not available or not viable for the many under resourced and often mutually unintelligible dialects of Arabic (DA), which are similarly morphologically rich and highly ambiguous (Chiang et al, 2006;Erdmann et al, 2017). Many recent efforts seek to develop morphological resources for DA, but most are under developed or specific to one dialect Eskander et al, 2013;Jarrar et al, 2014;Al-Shargi et al, 2016;Eskander et al, 2016a;Khalifa et al, 2016Khalifa et al, , 2017Zribi et al, 2017;Khalifa et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Complementary Strategies for Low Resourced Morphological Modeling

Erdmann¹,

Habash²

2018

Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Morphologically rich languages are challenging for natural language processing tasks due to data sparsity. This can be addressed either by introducing out-of-context morphological knowledge, or by developing machine learning architectures that specifically target data sparsity and/or morphological information. We find these approaches to complement each other in a morphological paradigm modeling task in Modern Standard Arabic, which, in addition to being morphologically complex, features ubiquitous ambiguity, exacerbating sparsity with noise. Given a small number of outof-context rules describing closed class morphology, we combine them with word embeddings leveraging subword strings and noise reduction techniques. The combination outperforms both approaches individually by about 20% absolute. While morphological resources already exist for Modern Standard Arabic, our results inform how comparable resources might be constructed for non-standard dialects or any morphologically rich, low resourced language, given scarcity of time and funding.

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Morphological disambiguation of Tunisian dialect

Cited by 23 publications

References 10 publications

An Algerian dialect: Study and Resources

An Algerian dialect: Study and Resources

Natural Language Processing for Dialectical Arabic: A Survey

Complementary Strategies for Low Resourced Morphological Modeling

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