Unit 752: Natural Language Processing Fundamentals and Applications

1. Introduction to Natural Language Processing

• Definition and scope of NLP
• Applications in AI, data science, and linguistics
• Importance of NLP in understanding human language for computers
• Overview of NLP challenges

2. Text Preprocessing in NLP

• Purpose and importance of text preprocessing
• Tokenization: methods and challenges
• Stop word removal: rationale and common stop word lists
• Stemming vs Lemmatization: techniques and differences
• Additional preprocessing steps: lowercasing, punctuation removal

3. Language Models and Word Embeddings

3.1 Language Models

• Definition and role of language models
• Statistical models: n-grams, bag-of-words
• Probabilistic modeling and prediction of word sequences

3.2 Word Embeddings

• Concept of vector representation of words
• Word2Vec: CBOW and Skip-gram models
• GloVe embeddings and their construction
• FastText embeddings: handling subword information
• Applications of embeddings in NLP tasks

4. Key NLP Tasks and Techniques

4.1 Text Classification

• Definition and use cases
• Machine learning algorithms: Naive Bayes, Support Vector Machines (SVM)
• Deep learning models: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)
• Evaluation metrics for classification

4.2 Sentiment Analysis

• Purpose and applications
• Lexicon-based approaches
• Machine learning models for sentiment classification
• Deep learning approaches: Long Short-Term Memory (LSTM) networks

4.3 Named Entity Recognition (NER)

• Task description and examples of named entities
• Rule-based NER systems
• Machine learning approaches: Conditional Random Fields (CRF)
• Neural approaches to NER

5. Sequence-to-Sequence Models

• Encoder-Decoder architecture overview
• Attention mechanisms and their significance
• Applications: machine translation, text summarization, question answering

6. Transformer Models

• Transformer architecture fundamentals
• Self-attention mechanism
• Pretrained models: BERT, GPT (including GPT-3), T5, Transformer-XL
• Impact on language understanding and generation tasks

7. Text Generation

• Overview of natural language generation (NLG)
• Language models for generation: GPT series
• Decoding strategies: beam search, sampling methods
• Applications: chatbots, creative writing, code generation

8. Machine Translation

• Challenges in translating languages
• Statistical Machine Translation (SMT) overview
• Neural Machine Translation (NMT) and sequence-to-sequence models

9. Chatbots and Conversational Agents

• Design and architecture of conversational systems
• Use of NLP for intent recognition and entity extraction
• Dialogue management and response generation

10. Ethical and Social Implications of NLP

• Bias in data and models: causes and mitigation
• Privacy concerns in NLP applications
• Fairness and accountability in NLP systems
• Societal impacts including misinformation and language preservation
• Strategies for responsible development and deployment