#nlp

A deep dive into how the morphological structure of human languages shapes the design of NLP systems — from classical rule-based methods to BPE and the frontiers of morphology-aware LLMs.

Implementation and evaluation of modern Neural Machine Translation models for the Arabic-English language pair.

A study on detecting sarcasm in dialectal Arabic tweets using machine learning techniques.

Implementation of SIAMESE neural networks to detect textual similarity and plagiarism in Arabic documents.

Exploring Latent Semantic Analysis (LSA) for identifying plagiarism in Arabic text.

Research on sentiment classification for Arabic text using genetic algorithms to optimize performance.

Development of text classification models for Arabic sentiment analysis using Transformer architectures like BERT and AraBERT.

A system combining semantic representation and deep classification models to identify misinformation.