Learn the foundations of how computers understand processing and produce human languages – Natural Language Processing (NLP), a science at the intersection of computer science, artificial intelligence and linguistics.
Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.
Every day, our computers and phones correct our spelling, curate our social media, or translate news articles for us. But have you ever wondered how these applications work on a basic level? It turns out that these are often really difficult tasks. The branch of computer science working on solutions is called Natural Language Processing – or NLP for short. At the end of this four-week course, you will be equipped with a solid understanding of how to work with text – that is, with written language. You’ll have the foundation to go forth and explore both traditional, time-tested approaches as well as the exciting, modern advanced approaches using deep learning. Putting all of this together, you’ll extend your reach in NLP through two assignments: to create your own text classification application and a generative, text suggestion system, like autocomplete, two very practical NLP applications that all of us use everyday.
The instructor team has over 30 years of experience with natural language processing. Min has led research on NLP at NUS for over 20 years and has a well-known track record of publishing research work in NLP, digital libraries and information retrieval. He has also been part of the executive board of the ACL, the premier organization supporting NLP research worldwide. Chris has published multiple papers in the area of social media and text analysis. At NUS, he now teaches natural language processing, text and data mining, and database systems to graduate and undergraduate students. Both Chris and Min have won awards for teaching at NUS and have received strong student feedback in their teaching of the NLP course at NUS.
What you'll learn
- Understand why Natural Language Processing is so challenging for computers
- Learn how to process natural language into representations suitable for computers.
- Master language models, which assign probabilities to word sequences.
- Build text classification programs through two different classification paradigms
Syllabus
Week 1: What is NLP?
What exactly is NLP, and why is it so important? What makes NLP so hard?
Week 2: Words
Introduction to natural language representation as words, through the tools of regular expressions and minimum edit distance.
Week 3: Language Models
Introduction to language models, which help to compute the similarities between natural language strings and predict their completions.
Week 4: Text Classification
Discuss how to design text classification features and how to use them in logistic regression and naïve Bayes classification methods.
