Learning Outcomes
This course will cover the main challenges involved in automatic text processing and present the key symbolic AI and machine learning techniques used to analyze, generate, and exploit natural language. The focus will be almost exclusively on written language processing.
Summary
The aim of this course is to introduce the main symbolic AI and machine learning methods used to analyze, generate, process, and produce documents written in natural language. Natural Language Processing (NLP) lies at the intersection of computer science and linguistics and forms a core branch of artificial intelligence. It encompasses all research and development efforts aimed at modeling and reproducing, through computational means, the human ability to produce and understand linguistic utterances for communication. The focus is therefore on human language—hence the term natural—as opposed to formal or programming languages.
Why Study Natural Language Processing?
As with most areas of AI, the study of NLP is motivated by two main objectives:
- Scientific motivation: to model language and test hypotheses about the mechanisms underlying human communication;
- Practical motivation: to build applications capable of efficiently processing the vast quantities of textual and spoken data now available in digital form (e.g., HTML pages, multimedia documents, social media content, etc.).
About Instructor
Course Includes
- 3 Lessons
- 2 Topics
