Natural Language Processing
This course provides the fundamental concepts and ideas in Natural Language Processing (NLP), an understanding of both the algorithms available for processing linguistic information and the underlying computational properties of natural languages. The course progresses from word-level and syntactic processing to question answering and machine translation. Topics to be covered include; Computational properties of natural languages; Co-reference, question answering, and machine translation; Processing linguistic information; Syntactic and semantic processing; Modern quantitative techniques in NLP; Neural network models for language understanding tasks
- Uri Wilensky, Introduction to agent-based Modeling: Modeling natural, social and engineered complex systems with NetLogo, MIT Press
- ANSI Common Lisp Book
- Paul Graham, ANSI Common Lisp, Pearson/Prentice-Hall
- IBM Watson Tone Analyzer
- Microsoft Language Understanding Intelligent Service (LUIS)
- Google Cloud Natural Language
- Merlin