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.
#nlp
Content tagged with "nlp"
Implementation and evaluation of modern Neural Machine Translation models for the Arabic-English language pair.
Sarcasm Detection in Arabic Tweets: A Comparison Between Deep Learning and Pre-trained Transformer-based Models
A study on detecting sarcasm in dialectal Arabic tweets using machine learning techniques.
Evaluating Siamese Architecture Neural Models for Arabic Textual Similarity and Plagiarism Detection
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.
Transformer-based sentiment classification for Arabic text (MSA and dialectal), built at CERIST using BERT and AraBERT.
A deep learning system combining semantic text representation with classification models to identify misinformation.
Intent classification for Arabic conversational text, supporting downstream applications like chatbots and voice assistants.
Sentiment classification models generalized across multiple languages, extending Arabic-focused work to a broader multilingual setting at CERIST.
Sentiment analysis combining text with additional modalities (audio/visual) for Arabic content, addressing the limits of text-only approaches.