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Found 9 papers, 0 papers with code
Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community
In this paper, an approach for hate speech detection against women in the Arabic community on social media (e. g. Youtube) is proposed.
Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination
Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine).
ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects
The most recent researches focus on constructing a language model and a training corpus for each dialect, in each script.
Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English
In this paper, an approach for hate speech detection against women in Arabic community on social media (e. g. Youtube) is proposed.
Arabic natural language processing: An overview
Arabic is recognised as the 4th most used language of the Internet.
Arabizi sentiment analysis based on transliteration and automatic corpus annotation
Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus.
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
In this paper, we present a novel approach to automatically construct an annotated sentiment corpus for Algerian dialect (a Maghrebi Arabic dialect).
Hybrid approach for transliteration of Algerian arabizi: a primary study
Through these rules, we generate a set of candidates for the transliteration of each Arabizi word into arabic.
Approche Hybride pour la translit\'eration de l'Arabizi Alg\'erien : une \'etude pr\'eliminaire (A hybrid approach for the transliteration of Algerian Arabizi: A primary study)
{\'A} partir de ces r{\`e}gles nous g{\'e}n{\'e}rons un ensemble de candidats pour la translit{\'e}ration de chaque mot en arabizi vers l{'}arabe, et un parmi ces candidats sera ensuite identifi{\'e} et extrait comme le meilleur candidat.
