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Course Outline
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
- Maximum entropy classifiers
- Sequence models (Hidden Markov Models)
- Probabilistic dependency
- Constituent parsing
- Vector-space models of meaning
Requirements
No prior background in NLP is required.
Prerequisite: Familiarity with at least one programming language (such as Java, Python, PHP, or VBA).
Expected: Solid mathematical proficiency (at the A-level standard), with particular emphasis on probability, statistics, and calculus.
Bonus: Familiarity with regular expressions is advantageous.
21 Hours
