Adouane Wafia, a été sélectionnée pour prendre part au programme Leaders for Democracy Fellowship 2012 (LDF) qui se déroule depuis plusieurs mois dans
Wafia Adouane: Natural Language Processing for Low-resourced Code-switched Colloquial Languages - The Case of Algerian Language Research. Culture and languages.
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Neural Models for Detecting Binary. Semantic Textual Similarity for Algerian and MSA. Adouane Wafia, Nasredine Semmar, Richard Johansson. (2016a). Romanized.
Gå med för att skapa kontakt University Wafia Adouane: Natural Language Processing for Low-resourced Code-switched Colloquial Languages - The Case of Algerian Language Wafia Adouane's 9 research works with 48 citations and 395 reads, including: Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA Wafia Adouane: Natural Language Processing for Low-resourced Code-switched Colloquial Languages - The Case of Algerian Language Research. Culture and languages.
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Lamia Adouane. Technicien de propreté. Paris Lamia Adouane. Ingénieur en onformatique chez Wafia BENHAMLAOUI. M2-HSE | IOSH-MS | NEBOSH-IGC.
Aassou, Omar, Ammar, Banan, Gawargy, Wafia Adouane, Jean-Philippe Bernardy, and Simon. Dobnik.
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Wafia Adouane, N. Semmar, Richard Johansson. Computer Science; 2016 IEEE/ ACS 13th International Conference of Computer Systems and Applications References · Wafia Adouane, N. Semmar, Richard Johansson, Victoria Bobicev · Computer Science · VarDial@COLING · 2016. Linus Van Der Putten, 27 årFlyttade hit för 2 år sedan. 0trappor Markplan. Dörr 3 från vänster.
Identifying Sentiments in Algerian Code-switched User-generated Comments @inproceedings{Adouane2020IdentifyingSI, title={Identifying Sentiments in Algerian Code-switched User-generated Comments}, author={Wafia Adouane and Samia Touileb and Jean-Philippe Bernardy}, booktitle={LREC}, year={2020} }
Request PDF | On Jan 1, 2018, Wafia Adouane and others published Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts
About humans, machines and dialogue at the International Science Sitemap
We present in this paper our work on Algerian language, an under-resourced North African colloquial Arabic variety, for which we built a comparably large corpus of more than 36,000 code-switched user-generated comments annotated for sentiments. We opted for this data domain because Algerian is a colloquial language with no existing freely available corpora.
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