Natural Language Understanding

Stanford Center for Professional Development

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Stanford Center for Professional Development

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About the course

From conversational agents to automated trading and search queries, natural language understanding underpins many of today’s most exciting technologies. How do we build these models to understand language efficiently and reliably? In this project-oriented course, you will develop systems and algorithms for robust machine understanding of human language. The course draws on theoretical concepts from linguistics, natural language processing, and machine learning.

In the first half of the course, you will explore three fundamental tasks in natural language understanding: the creation of word vectors, relation extraction (with an emphasis on distant supervision), and natural language inference. Each topic includes a hands-on component where you will build baseline models that in turn inform your own original models that you will enter into informal class-wide competitions.

In the second half of the course, you will pursue an original project in natural language understanding with a focus on following best practices in the field. Additional lectures and materials will cover important topics to help expand and improve your original system, including evaluations and metrics, semantic parsing, and grounded language

What you will learn

  • Best practices for building and testing natural language understanding models
  • How to apply key techniques, such as relation extraction and natural language inference
  • How to use distant supervision to take advantage of large knowledge bases and large unlabeled data sets

Prerequisites

  • Proficiency in Python: All class assignments will be in Python (using NumPy and PyTorch). If you have extensive programming experience in a different language (C/C++/MATLAB/Java/JavaScript) we recommend you familiarize yourself with Python basics before the course begins.
  • College Calculus, Linear Algebra: You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations.
  • Basic Probability and Statistics: You should know basics of probabilities, gaussian distributions, mean, and standard deviation.
  • Foundations of Machine Learning (recommended but not required): Knowledge of basic machine learning and/or deep learning is helpful, but not required.

Experts

Christopher Potts

Academic Appointments Professor, Linguistics Professor (By courtesy), Computer Science Member, Bio-X Administrative Appointments Professor, Department of Linguistics, Stanford University (2016 - Present) Director, Stanford Center for the Study of Language and Information (CSLI) (2013 - Present...

Bill MacCartney

Bill MacCartney is a Senior Engineering Manager at Apple, where he leads the Siri Proactive Intelligence team in propagating predictive intelligence across the iOS platform and beyond. Bill is also a Consulting Professor of Computer Science at Stanford University, where he teaches CS224U, “Natura...

Natural Language Understanding at Stanford Center for Professional Development

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