Natural Language Processing(NLP) B.Tech Handwritten Notes For University Exams

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1. Introduction to Natural Language Processing

• Definition and scope of NLP
• History and evolution of NLP
• Applications of NLP: Machine translation, sentiment analysis, chatbots, text summarization
• Challenges in NLP: Ambiguity, context, and diversity in natural languages

2. Basics of Language and Linguistics

• Elements of natural language: Phonetics, morphology, syntax, semantics, and pragmatics
• Language models and grammars
• Parts of speech (POS) tagging
• Lemmatization and stemming
• Tokenization and word segmentation

3. Text Preprocessing

• Cleaning text data: Stopword removal, punctuation handling
• Normalization techniques: Lowercasing, stemming, lemmatization
• Handling special characters and emojis
• N-grams and bag-of-words (BoW) model
• Term frequency-inverse document frequency (TF-IDF)

4. Language Modeling

• Statistical language models: Unigram, bigram, trigram
• Word embeddings:
  • Word2Vec: Skip-gram and CBOW
  • GloVe (Global Vectors for Word Representation)
  • FastText
• Contextual embeddings: ELMo, BERT, GPT

5. NLP Techniques and Algorithms

• Text classification
  • Naive Bayes, Logistic Regression, SVM
  • Neural network-based approaches
• Named Entity Recognition (NER)
• Sentiment analysis
• Topic modeling: Latent Dirichlet Allocation (LDA)
• Sequence labeling and tagging

6. Parsing and Syntax Analysis

• Dependency parsing
• Constituency parsing
• Chunking and shallow parsing
• Syntax trees and grammar rules

7. Advanced NLP Models

• Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Units (GRUs)
• Transformers
  • Attention mechanism
  • Transformer architecture: BERT, GPT, T5
• Sequence-to-sequence models
  • Applications in machine translation and text summarization

8. NLP Applications

• Machine translation: Rule-based, statistical, and neural approaches
• Text summarization: Extractive and abstractive methods
• Question answering systems
• Chatbots and conversational agents
• Speech-to-text and text-to-speech systems
• Information retrieval and search engines

9. NLP Tools and Libraries

• Natural Language Toolkit (NLTK)
• spaCy
• Stanford CoreNLP
• Gensim for topic modeling
• Hugging Face Transformers
• TensorFlow and PyTorch for deep learning-based NLP

10. Ethical and Social Implications

• Bias in NLP models
• Privacy and data security concerns
• Ethical considerations in deploying NLP systems
• Handling offensive and harmful content

11. Case Studies and Projects

• Sentiment analysis on Twitter data
• Building a chatbot using Rasa or Dialogflow
• Text summarization of news articles
• Machine translation using Transformer models
• Named entity recognition on legal or medical documents
• Topic modeling on large datasets