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Description

This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.

Link to Courseware: http://see.stanford.edu/see/courseinfo.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a

Prerequisites and Difficulty

Prerequisites: • Adequate experience with programming and formal structures (e.g., CS106B/X and CS103B/X). • Programming projects will be written in Java 1.5, so knowledge of Java (or a willingness to learn on your own) is required. • Knowledge of standard concepts in artificial intelligence and/or computational linguistics (e.g., CS121/221 or Ling 180). • Basic familiarity with logic, vector spaces, and probability. Intended Audience: • Graduate students and advanced undergraduates specializing in computer science, linguistics, or symbolic systems.

Materials and Technical Requirements

None

How Curious Reef Works

The purpose of the class is to learn collaboratively with others.
The course creator has organized the course information for everyone.

• Complete one lesson and its assignments per week
• Submit and self-grade your own work
• Help each other in the class forum

License