IST 664: Natural Language Processing

This course focuses on the linguistic and computational aspects of natural language processing (NLP) technologies. Students develop an understanding of how NLP can process written text and produce a linguistic analysis that can be used in other applications. Discussions cover the multiple levels of linguistic analysis required for a computer to accept natural language input, interpret it and carry out a particular application. Topics include levels of linguistic analysis with a focus on techniques in application. Students in this course will explore all the levels of linguistic analysis, going from tokenization, word-level semantics, part-of-speech tagging, syntax and semantics up to the discourse level. They will also use NLP techniques on unstructured data using Python, including information retrieval, question-answering, sentiment analysis, summarization and dialogue systems.

Learning Objectives:

  • Demonstrate the levels of linguistic analysis and computational techniques used to understand text at each level and what the challenges are for those techniques
  • Process text through the language levels using the resources of the Natural Language Toolkit (NLTK) and some rudimentary use of the programming language Python
  • Describe how NLP is used in real-world applications

Tools & Concepts:

  • Machine Learning
  • Python / NLTK
  • Linguistic analysis
  • Tokenization
  • Word-level semantics
  • Part-of-speech tagging, syntax and discourse
  • Sentiment analysis and summarization
  • Dialogue systems

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