Course Outline

Short Introduction to NLP Methods for Government

  • Word and sentence tokenization
  • Text classification
  • Sentiment analysis
  • Spelling correction
  • Information extraction
  • Parsing
  • Meaning extraction
  • Question answering

Overview of NLP Theory for Government

  • Probability
  • Statistics
  • Machine learning
  • N-gram language modeling
  • Naive Bayes
  • Maxent classifiers
  • Sequence models (Hidden Markov Models)
  • Probabilistic dependency
  • Constituent parsing
  • Vector-space models of meaning

Requirements

No background in natural language processing is required.

Required: Familiarity with any programming language (Java, Python, PHP, VBA, etc.).

Expected: Reasonable math skills (A-level standard), particularly in probability, statistics, and calculus.

Beneficial: Familiarity with regular expressions for government applications.

 21 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

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