NLP — core task?
Extract meaning from human language
NLP — definition?
Enables machines to interpret and generate human language.
Classical NLP pipeline — step?
Tokenization, morphology, syntax, semantics, pragmatics
Transformers — key feature?
Parallel processing with self-attention mechanisms.
Word embeddings — type?
Vector representations capturing similarity
Large Language Models — examples?
BERT, GPT, T5.
Static embeddings — limitation?
Limited polysemy handling, fixed meaning.
Tokenization — role?
Splits text into words or subword units.
Syntax parsing — purpose?
Builds sentence structure trees or dependencies.
Responsible NLP — concerns?
Bias, fairness, privacy, energy use.
Testez vos connaissances avec un QCM de 9 questions sur AI Language Interaction and Technologies.
1. What is the primary goal of natural language processing (NLP) in human–computer interaction?
2. Which of the following models is known for its encoder-only architecture that is capable of understanding and generating language, and has been mentioned in the revision sheet?
Révisez le cours complet dans la fiche de révision de AI Language Interaction and Technologies.
Voir la fiche →Bases de données
Bases de données
Programmation
Programmation
Importe ton cours et l'IA génère des flashcards en 30 secondes.
Générateur de flashcards