Natural Language Processing

Learning Outcomes

  • Ability to Understand fundamental concepts of the Natural Language Processing
  • Ability understand Natural Language processing techniques
  • Ability utilize and explain the function of software tools for NLP
  • Critically appraise existing Natural Language Processing applications
  • Apply NLP concepts for application development.

Outline Syllabus

  1. Introduction to NLP
  2. Words and Morphology
  3. Levels of Language Processing
    • Morphology
    • Syntax
    • Semantics
    • Pragmatics
  4. Speech Processing
  5. Conversational Agents and Chatbots
  6. Machine Translation
    • Approaches
    • Issues

References

  • Bird, Klein, and Loper (2009): Natural Language Processing with Python.
  • Pinker (2007). The Language Instinct.
  • Jurafsky and Martin (2009): Speech and Language Processing, second edition.
  • Perkins (2010): Python Text Processing with NLTK 2.0 Cookbook.
  • Segaran (2007) Programming Collective Intelligence.
  • Russell and Norvig (2010) Artificial Intelligence: a modern approach, third edition.

Course contents

  1. Introduction to NLP [ pdf, 27.05.2020] 
  2. Speech Processing 
    1. Text-to-Speech [pdf, update 27.05.2020] 
    2. Speech Recognition [pdf , update 27.05.2020] 
  3. Regular expression and FSA for word processing [ pdf, update 27.05.2020] 
  4. Word level Processing (Morphology) [ pdf, update 27.05.2020] 
  5. Syntax Processing [ pdf, update 27.05.2020] 
  6. Language Parsing 
    1. Introduction to Parsing [ pdf, update 27.05.2020] 
    2. Parsing Techniques[ pdf, update 27.05.2020] 
    3. Sample Prolog Parser[ pdf, update 27.05.2020] 
  7. Conversational Agents and Chatbots [ pdf, update 27.05.2020] 
  8. Machine Translation [ pdf, update 27.05.2020] 

 

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