Summary
In this course, you will create your own natural language training corpus for machine learning. This course guides you through the process of adding metadata to your training corpus to help ML algorithms work more efficiently. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project.
- Define a clear annotation goal before collecting your dataset (corpus)
- Learn tools for analyzing the linguistic content of your corpus
- Build a model and specification for your annotation project
- Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework
- Create a gold standard corpus that can be used to train and test ML algorithms
- Select the ML algorithms that will process your annotated data
- Evaluate the test results and revise your annotation task.
Course Details
- Time: Tuesday, Friday. 11:00-12:20 pm
- Location: Volen 106
- Instructor
- Professor: James Pustejovsky
- 258 Volen Center
- E-mail
- Office Hours: Monday, Wednesday 11:00 – 12:00 pm
- Teaching Assistant: Keigh Rim
- 111 Volen Center
- E-Mail
- Office Hours: Tue 2-4 pm or by appointment
- Required Text: Natural Language Annotation for Machine Learning
- James Pustejovsky and Amber Stubbs
- O’Reilly Publishers
- 2012
