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23AMB302 – NATURAL LANGUAGE PROCESSING
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The NLP Landscape: History and Applications
Text Preprocessing: Tokenization
Stemming – Lemmatization
Stopword Removal
Regular Expressions for Text Pattern Matching
WordNet and Lexical Resources
Basic String Matching and Edit Distance
Language Modeling: N-grams and Smoothing Techniques
Basic Text Classification: Naive Bayes
Logistic Regression
Vector Space Models: Bag-of-Words (BoW)
TF-IDF
Word Embeddings: Word2Vec Skip-gram – CBOW
GloVe
Evaluation Metrics: Precision
Recall – F1-Score
Neural Networks for NLP
Recurrent Neural Networks (RNNs)
LSTMs and GRUs
Sequence-to-Sequence Models (Seq2Seq)
Attention Mechanism
Introduction to the Transformer Architecture
Self-Attention
Encoder-Decoder Structure
Transfer Learning in NLP
Transformer-based Models: BERT
GPT, T5
Fine-Tuning Strategies for Downstream Tasks
Text Classification, NER, Q&A
Prompt Engineering
In-Context Learning
Introduction to Text Generation
Hugging Face Ecosystem: Transformers Library
Datasets and Model Hub
Advanced Tasks: Named Entity Recognition (NER)
Sentiment Analysis
Machine Translation
Text Summarization
Building RAG (Retrieval-Augmented Generation) Applications
Bias and Fairness in NLP Models
Explainability and Interpretability for NLP
Current Trends and Ethics
23AMB302 NLP
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