People Innovation Excellence

NATURAL LANGUAGE PROCESSING (2/2 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – describe what is Natural Language Processing and its components; LO2 – explain Explain fundamental concepts of how to work with Natural Language Processing; LO3 – apply Natural Language Processing concepts in certain real-world applications; LO4 – construct Natural Language Processing applications.

Topics:

  1. Language and Computation;
  2. N-Grams and Part-of-Speech;
  3. Corpus; Naïve Bayes & Sentiment Classification;
  4. Classification & Logistic Regression;
  5. Vector Semantics & Embeddings;
  6. Progress Project Presentation;
  7. Neural Networks & Neural Language Models;
  8. Constituency Grammars & Parsing;
  9. Dependency Parsing;
  10. Semantic Role Labeling;
  11. Chatbots;
  12. Final Project Presentation;
  13. Intro to Natural Language Toolkit (Tokenization, Stop Words);
  14. Stemming, Lemmatization;
  15. POS Tagging;
  16. TF-IDF and Cosine Similarity;
  17. Sentiment Classification with Naïve Bayes;
  18. Quiz;
  19. Language Model (N-Grams);
  20. Word Embedding;
  21. Grammar Parsing with NLTK;
  22. Dependency Parsing with Spacy;
  23. Name Entity Recognition; Final Exam.

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