Advanced techniques in Natural Language Processing (NLP) explore how machines process and generate human language, focusing on concepts like embeddings, transformers, and large language models. The chapter emphasizes the evolution of NLP from traditional techniques to deep learning methods. It also discusses real-world applications, evaluation metrics, and the importance of pretrained models in improving efficiency and performance in NLP tasks.
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Sections
Class Notes
Memorization
What we have learnt
- NLP enables machines to und...
- Word embeddings and transfo...
- BERT and GPT have redefined...
Final Test
Revision Tests
Chapter FAQs
What we have learnt
- NLP enables machines to understand and generate human language.
- Word embeddings and transformers are foundational technologies.
- BERT and GPT have redefined performance benchmarks in NLP.
- Pretrained models save time and resources in production settings.
- Evaluation and interpretability are critical for responsible NLP use.
Key Concepts
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Term: Word Embeddings
Definition: Techniques that represent words in a continuous vector space where semantically similar words are mapped to proximate points.
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Term: Transformers
Definition: A deep learning model architecture that relies on self-attention mechanisms and is highly effective for sequence-to-sequence tasks in NLP.
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Term: Transfer Learning
Definition: A method where a model developed for a specific task is repurposed on a second related task, widely used for fine-tuning pretrained models.
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Term: Evaluation Metrics
Definition: Quantitative measurements used to assess the performance of NLP models, such as accuracy, precision, recall for classification tasks, and BLEU for translation.