Search Results for author:
Found 12 papers, 4 papers with code
Weighted combination of BERT and N-GRAM features for Nuanced Arabic Dialect Identification
Around the Arab world, different Arabic dialects are spoken by more than 300M persons, and are increasingly popular in social media texts.
BERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic Identification
Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications.
ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
We also demonstrate the effectiveness of ProMap in re-ranking results from other BLI methods such as with aligned static word embeddings.
Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media
The prevalence of toxic content on social media platforms, such as hate speech, offensive language, and misogyny, presents serious challenges to our interconnected society.
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic
Our system\footnote{The source code of our system is available at \url{https://github. com/AbdelkaderMH/iSarcasmEval}} consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English.
UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer
This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerging entities, and complex entities.
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense
Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e. g. a community, a city, a country, the employees of a given company).
CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity
The proposed system consists of a deep learning model, based on pre-trained transformer encoder, for word and Multi-Word Expression (MWE) complexity prediction.
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language
The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA).
BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification
Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications.
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding
In this paper, we propose a new unsupervised domain adaptation method for Arabic cross-domain and cross-dialect sentiment analysis from Contextualized Word Embedding.
An open access NLP dataset for Arabic dialects : Data collection, labeling, and model construction
This includes applications for the Arabic language and its national dialects.
