Search Results for author:
Found 21 papers, 4 papers with code
iCompass at Arabic Hate Speech 2022: Detect Hate Speech Using QRNN and Transformers
This paper provides a detailed overview of the system we submitted as part of the OSACT2022 Shared Tasks on Fine-Grained Hate Speech Detection on Arabic Twitter, its outcome, and limitations.
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic
We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2).
Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis
On various Social Media platforms, people, tend to use the informal way to communicate, or write posts and comments: their local dialects.
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect
Despite their success, most of the available models have been trained on Indo-European languages however similar research for under-represented languages and dialects remains sparse.
TEET! Tunisian Dataset for Toxic Speech Detection
The complete freedom of expression in social media has its costs especially in spreading harmful and abusive content that may induce people to act accordingly.
Bambara Language Dataset for Sentiment Analysis
For easier communication, posting, or commenting on each others posts, people use their dialects.
iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic
This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021.
AI4D -- African Language Program
Advances in speech and language technologies enable tools such as voice-search, text-to-speech, speech recognition and machine translation.
A Multilingual African Embedding for FAQ Chatbots
Searching for an available, reliable, official, and understandable information is not a trivial task due to scattered information across the internet, and the availability lack of governmental communication channels communicating with African dialects and languages.
iCompass at SemEval-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model
We describe our submitted system to the SemEval 2020.
Learning Word Representations for Tunisian Sentiment Analysis
In this paper, we focus on the Tunisian dialect sentiment analysis used on social media.
TUNIZI: a Tunisian Arabizi sentiment analysis Dataset
On social media, Arabic people tend to express themselves in their own local dialects.
L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
Hate speech and abusive language have become a common phenomenon on Arabic social media.
Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects
Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content.
Tw-StAR at SemEval-2019 Task 5: N-gram embeddings for Hate Speech Detection in Multilingual Tweets
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){''}.
Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis
Therefore, we develop an algorithm to detect the sentiment of Named Entities based on the majority of attitudes towards them.
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification
A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets.
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge
Therefore, the ability to keep customers in a brand is becoming more challenging these days.
Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets
In this paper, we present our contribution in SemEval 2017 international workshop.
