Natural Language Processing (NLP)



NLP is a branch of computer science that uses computational methods to analyze and manipulate human language. It enables machines to understand and generate text, perform tasks like sentiment analysis, speech recognition, and language translation, and is used in various applications such as chatbots and search engines. NLP merges techniques from computer science, linguistics, and mathematics to make machines communicate and understand human language naturally.

Introduction to NLP

  • Overview of NLP and its applications
  • History of NLP
  • Challenges in NLP

Language Basics for NLP

  • Parts of speech tagging
  • Phrase structure and parsing
  • Morphology

Text preprocessing and feature extraction

  • Text normalization and tokenization
  • Stop word removal and stemming
  • N-grams and feature engineering

Sentiment analysis

  • Overview of sentiment analysis
  • Supervised and unsupervised approaches
  • Lexicon-based approaches

Named entity recognition

  • Introduction to named entity recognition
  • Rule-based and statistical approaches
  • Evaluation metrics for named entity recognition

Information retrieval and text classification

  • Introduction to information retrieval
  • Vector space model and cosine similarity
  • Text classification using Naive Bayes and SVM

Language modeling and sequence-to-sequence models

  • Introduction to language modeling
  • N-gram and neural language models
  • Sequence-to-sequence models for NLP

Neural network architectures for NLP

  • Convolutional neural networks for NLP
  • Recurrent neural networks for NLP
  • Attention mechanisms for NLP

Text summarization and machine translation

  • Overview of text summarization
  • Extractive and abstractive summarization
  • Introduction to machine translation

Advanced topics in NLP

  • Natural language generation
  • Dialogue systems
  • Ethics in NLP
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