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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 background in NLP is required.
Required: Familiarity with any programming language (Java, Python, PHP, VBA, etc...).
Expected: Reasonable maths skills (A-level standard), especially in probability, statistics and calculus.
Beneficial: Familiarity with regular expressions.
21 Hours