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.
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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?
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