Teaching‎ > ‎

Natural Language Processing


  1. Introduction and Syllabus
  2. Python programming
  3. Preprocessing (data crawling, HTML parsing, stemming/lemmeatization, tokenization, etc. )  iPython notebook for HTML parsing and regular expression tokenizer
  4. POS tagging using HMM
  5. N-gram language model
  6. Machine Learning (linear classifiers, SVM, ANN, Deep Learning, neural language models)
  7. Feature engineering in NLP
  8. Word2vec
  9. LDA
  10. Review analysis and sentimental analysis