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Course Outline
Short Introduction to NLP methods
- word and sentence tokenization
- text classification
- sentiment analysis
- spelling correction
- information extraction
- parsing
- meaning extraction
- question answering
Overview of NLP theory
- 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 prior background in NLP is necessary.
Required: Familiarity with at least one programming language (such as Java, Python, PHP, VBA, etc.).
Expected: A solid foundation in mathematics (at the A-level standard), particularly in probability, statistics, and calculus.
Helpful: Familiarity with regular expressions.
21 Hours
