activity
20222025
most citedMental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

28 citations · 38 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CL2025

Fine-Tuning Large Language Models with QLoRA for Offensive Language Detection in Roman Urdu-English Code-Mixed Text

Nisar Hussain, Amna Qasim, Gull Mehak +3

The use of derogatory terms in languages that employ code mixing, such as Roman Urdu, presents challenges for Natural Language Processing systems due to unstated grammar, inconsist…

cs.IR2025

Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning

Anvi Alex Eponon, Moein Shahiki-Tash, Ildar Batyrshin +3

This study presents a question-based knowledge encoding approach that improves retrieval-augmented generation (RAG) systems without requiring fine-tuning or traditional chunking. W…

cs.CL2025

Multilingual Hate Speech Detection in Social Media Using Translation-Based Approaches with Large Language Models

Muhammad Usman, Muhammad Ahmad, M. Shahiki Tash +3

Social media platforms are critical spaces for public discourse, shaping opinions and community dynamics, yet their widespread use has amplified harmful content, particularly hate…

cs.CL20251 cited

EDU-NER-2025: Named Entity Recognition in Urdu Educational Texts using XLM-RoBERTa with X (formerly Twitter)

Fida Ullah, Muhammad Ahmad, Muhammad Tayyab Zamir +4

Named Entity Recognition (NER) plays a pivotal role in various Natural Language Processing (NLP) tasks by identifying and classifying named entities (NEs) from unstructured data in…

cs.CL2024

A multitask learning framework for leveraging subjectivity of annotators to identify misogyny

Jason Angel, Segun Taofeek Aroyehun, Grigori Sidorov +1

Identifying misogyny using artificial intelligence is a form of combating online toxicity against women. However, the subjective nature of interpreting misogyny poses a significant…

cs.LG2024

Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning

Jesús Armenta-Segura, Grigori Sidorov

In the japanese anime industry, predicting whether an upcoming product will be popular is crucial. This paper presents a dataset and methods on predicting anime popularity using a…