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Course Outline
Introduction to NLP Techniques
- Word and sentence tokenization
- Text classification
- Sentiment analysis
- Spelling correction
- Information extraction
- Parsing
- Meaning extraction
- Question answering
NLP Theory Overview
- 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.
Prerequisite: Familiarity with at least one programming language (e.g., Java, Python, PHP, VBA).
Expected: Solid mathematical skills (A-level standard), particularly in probability, statistics, and calculus.
Advantageous: Knowledge of regular expressions.
21 Hours
