1 code implementation • EACL (WANLP) 2021 • Hala Mulki, Bilal Ghanem
Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration.
no code implementations • WS 2019 • Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babao{\u{g}}lu
Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content.
no code implementations • WS 2019 • Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, Halima Alshabani
Hate speech and abusive language have become a common phenomenon on Arabic social media.
no code implementations • SEMEVAL 2019 • Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babao{\u{g}}lu
In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 {``}Multilingual detection of hate speech against immigrants and women in Twitter (HatEval){''}.
no code implementations • 23 Apr 2019 • Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu
Therefore, we develop an algorithm to detect the sentiment of Named Entities based on the majority of attitudes towards them.
no code implementations • SEMEVAL 2018 • Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, Ismail Babao{\u{g}}lu
A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets.
no code implementations • WS 2017 • Mourad Gridach, Hatem Haddad, Hala Mulki
Therefore, the ability to keep customers in a brand is becoming more challenging these days.
no code implementations • SEMEVAL 2017 • Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu
In this paper, we present our contribution in SemEval 2017 international workshop.