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INFO-B 443 Natural Language Processing

3 credits

  • Prerequisites: INFO-B 211
  • Delivery: On-Campus, Online
  • This course introduces the theory and methodology of natural language understanding and generation. Topics include stemming, lemmatization, parts of speech tagging, parsing, and machine translation. Employing specialized libraries, students develop applications for topic modeling, sentiment analysis, and text summarization.

    Learning Outcomes

    1. Extract information from text automatically using concepts and methods from natural language processing (NLP) including stemming, n-grams, POS tagging, and parsing.
    2. Develop speech-based applications that use speech analysis (phonetics, speech recognition, and synthesis).
    3. Analyze the syntax, semantics, and pragmatics of a statement written in a natural language.
    4. Develop a conversational agent that uses natural language understanding and generation.
    5. Apply machine learning algorithms to natural language processing.
    6. Write scripts and applications in Python to carry out natural language processing using libraries such as NLTK, Gensim, and spaCY.
    7. Design NLP-based AI systems for question answering, text summarization, and machine translation.
    8. Evaluate the performance of NLP tools and systems.


    There is not a syllabus available for this course.