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Younes Samih
Researcher at University of Düsseldorf
Publications - 43
Citations - 790
Younes Samih is an academic researcher from University of Düsseldorf. The author has contributed to research in topics: Modern Standard Arabic & Task (project management). The author has an hindex of 15, co-authored 42 publications receiving 587 citations. Previous affiliations of Younes Samih include Khalifa University & Dublin City University.
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Arabic Offensive Language on Twitter: Analysis and Experiments
TL;DR: This paper introduces a method for building a dataset that is not biased by topic, dialect, or target, and produces the largest Arabic dataset to date with special tags for vulgarity and hate speech.
Proceedings ArticleDOI
Multilingual Code-switching Identification via LSTM Recurrent Neural Networks
TL;DR: The HHU-UHG system introduces a novel unified neural network architecture for language identification in code-switched tweets for both SpanishEnglish and MSA-Egyptian dialect.
Proceedings ArticleDOI
A Neural Architecture for Dialectal Arabic Segmentation
Younes Samih,Mohammed Attia,Mohamed Eldesouki,Ahmed Abdelali,Hamdy Mubarak,Laura Kallmeyer,Kareem Darwish +6 more
TL;DR: This paper shows how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources.
Proceedings ArticleDOI
The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech
Ahmed Ali,Suwon Shon,Younes Samih,Hamdy Mubarak,Ahmed Abdelali,James Glass,Steve Renals,Khalid Choukri +7 more
TL;DR: The fifth edition of the Multi-Genre Broadcast Challenge (MGB-5), an evaluation focused on Arabic speech recognition and dialect identification, extends the previous MGB-3 challenge in two ways: first it focuses on Moroccan Arabic Speech recognition, and the granularity of the Arabic dialect identification task is increased from 5 dialect classes to 17.
Proceedings Article
Arabic Word Generation and Modelling for Spell Checking
TL;DR: This work creates an adequate, open-source and large-coverage word list for Arabic containing 9,000,000 fully inflected surface words and creates a character-based tri-gram language model to approximate knowledge about permissible character clusters in Arabic, creating a novel method for detecting spelling errors.