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Kul'turovedčeskaja lingvictika i lingvokul'turologija : Dresdener und Kazaner Beiträge zur Axiologie = Kulturwissenschaftliche Linguistik und Sprachkulturologie
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BLLDB
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UB Frankfurt Linguistik
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PADIC: extension and new experiments
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In: 7th International Conference on Advanced Technologies ; 7th International Conference on Advanced Technologies ICAT ; https://hal.archives-ouvertes.fr/hal-01718858 ; 7th International Conference on Advanced Technologies ICAT, Apr 2018, Antalya, Turkey (2018)
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A Combined CNN and LSTM Model for Arabic Sentiment Analysis
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In: Lecture Notes in Computer Science ; 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) ; https://hal.inria.fr/hal-02060041 ; 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.179-191, ⟨10.1007/978-3-319-99740-7_12⟩ (2018)
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CinemAfriq@BU Film Series: Viva Laldjérie film (poster and promo)
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Processing Tools for Greek and Other Languages of the Christian Middle East
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In: EISSN: 2416-5999 ; Journal of Data Mining and Digital Humanities ; https://hal.archives-ouvertes.fr/hal-01671592 ; Journal of Data Mining and Digital Humanities, Episciences.org, 2018, Special Issue on Computer-Aided Processing of Intertextuality in Ancient Languages (2018)
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Building a syntactic-semantic interface for a semi-automatically generated TAG for Arabic
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In: ISSN: 1683-3198 ; International Arab Journal of Information Technology ; https://hal.archives-ouvertes.fr/hal-01809771 ; International Arab Journal of Information Technology, Colleges of Computing and Information Society (CCIS), In press ; http://www.iajit.org/ (2018)
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Berber Etymologies in Maltese
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In: ISSN: 2421-9835 ; International Journal of Arabic Linguistics ; https://halshs.archives-ouvertes.fr/halshs-01966533 ; International Journal of Arabic Linguistics, 2018, 4 (1), pp.190-223 ; https://revues.imist.ma/index.php?journal=IJAL&page=article&op=view&path%5B%5D=12894&path%5B%5D=7163 (2018)
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Ḥassāniyya Arabic in contact with Berber: The case of quadriliteral verbs ; L'arabe ḥassāniyya au contact du berbère : le cas des verbes quadrilitères
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In: Arabic in Contact ; https://halshs.archives-ouvertes.fr/halshs-03087750 ; Stefano Manfredi; Mauro Tosco. Arabic in Contact, 6, John Benjamins, pp.136-159, 2018, Studies in Arabic Linguistics, 978 90 272 0135 5 (2018)
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Machine Translation of Arabic Dialects
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Abstract:
This thesis discusses different approaches to machine translation (MT) from Dialectal Arabic (DA) to English. These approaches handle the varying stages of Arabic dialects in terms of types of available resources and amounts of training data. The overall theme of this work revolves around building dialectal resources and MT systems or enriching existing ones using the currently available resources (dialectal or standard) in order to quickly and cheaply scale to more dialects without the need to spend years and millions of dollars to create such resources for every dialect. Unlike Modern Standard Arabic (MSA), DA-English parallel corpora is scarcely available for few dialects only. Dialects differ from each other and from MSA in orthography, morphology, phonology, and to some lesser degree syntax. This means that combining all available parallel data, from dialects and MSA, to train DA-to-English statistical machine translation (SMT) systems might not provide the desired results. Similarly, translating dialectal sentences with an SMT system trained on that dialect only is also challenging due to different factors that affect the sentence word choices against that of the SMT training data. Such factors include the level of dialectness (e.g., code switching to MSA versus dialectal training data), topic (sports versus politics), genre (tweets versus newspaper), script (Arabizi versus Arabic), and timespan of test against training. The work we present utilizes any available Arabic resource such as a preprocessing tool or a parallel corpus, whether MSA or DA, to improve DA-to-English translation and expand to more dialects and sub-dialects. The majority of Arabic dialects have no parallel data to English or to any other foreign language. They also have no preprocessing tools such as normalizers, morphological analyzers, or tokenizers. For such dialects, we present an MSA-pivoting approach where DA sentences are translated to MSA first, then the MSA output is translated to English using the wealth of MSA-English parallel data. Since there is virtually no DA-MSA parallel data to train an SMT system, we build a rule-based DA-to-MSA MT system, ELISSA, that uses morpho-syntactic translation rules along with dialect identification and language modeling components. We also present a rule-based approach to quickly and cheaply build a dialectal morphological analyzer, ADAM, which provides ELISSA with dialectal word analyses. Other Arabic dialects have a relatively small-sized DA-English parallel data amounting to a few million words on the DA side. Some of these dialects have dialect-dependent preprocessing tools that can be used to prepare the DA data for SMT systems. We present techniques to generate synthetic parallel data from the available DA-English and MSA- English data. We use this synthetic data to build statistical and hybrid versions of ELISSA as well as improve our rule-based ELISSA-based MSA-pivoting approach. We evaluate our best MSA-pivoting MT pipeline against three direct SMT baselines trained on these three parallel corpora: DA-English data only, MSA-English data only, and the combination of DA-English and MSA-English data. Furthermore, we leverage the use of these four MT systems (the three baselines along with our MSA-pivoting system) in two system combination approaches that benefit from their strengths while avoiding their weaknesses. Finally, we propose an approach to model dialects from monolingual data and limited DA-English parallel data without the need for any language-dependent preprocessing tools. We learn DA preprocessing rules using word embedding and expectation maximization. We test this approach by building a morphological segmentation system and we evaluate its performance on MT against the state-of-the-art dialectal tokenization tool.
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Keyword:
Arabic language--Dialects; Arabic language--Machine translating; Artificial intelligence; Computer science
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URL: https://doi.org/10.7916/D8Q25H44
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A semi-automatically generated TAG for Arabic: Dealing with linguistic phenomena
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In: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018) ; https://hal.archives-ouvertes.fr/hal-01762597 ; 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018), Mar 2018, Hanoi, Vietnam (2018)
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The Amazigh influence on Moroccan Arabic: Phonological and morphological borrowing
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In: ISSN: 2421-9835 ; International Journal of Arabic Linguistics ; https://halshs.archives-ouvertes.fr/halshs-01798660 ; International Journal of Arabic Linguistics, 2018, Arabic-Amazigh contact, 4 (1), pp.39-58 ; http://revues.imist.ma/index.php?journal=IJAL&page=article&op=view&path%5B%5D=12890 (2018)
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Which language for understanding and practicing Islam in multilingual Europe? Case studies from immigrants from the Indian sub-continent & Suriname
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In: European languages as Muslim lingua francas ; https://hal.archives-ouvertes.fr/hal-01942135 ; European languages as Muslim lingua francas, Prof. Dr. Michael Kemper, University of Amsterdam, Dec 2018, Amsterdam, Netherlands ; http://www.uva.nl/en/shared-content/faculteiten/en/faculteit-der-geesteswetenschappen/events/conferences/2018/12/european-languages-as-muslim-lingua-francas.html (2018)
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Unsupervised Creation of Normalisation Dictionaries for Micro-Blogs in Arabic, French and English
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In: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018) ; https://hal.archives-ouvertes.fr/hal-01795348 ; 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018), Mar 2018, Hanoi, Vietnam (2018)
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