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.
While I will generally be in my office during posted office hours, I am both there lots of other times and sometimes have meetings or other obligations that come up during that time. You’re welcome to drop by, but if you need to see me, I recommend you send an email or let me know after class so we can agree on a time.
If my door is closed I’m probably not there, but my dog might be. So you could knock, just be aware that you might get a barky response.
Required Text: Natural Language Annotation for Machine Learning
- James Pustejovsky and Amber Stubbs
- O’Reilly Publishers
Success in this 4 credit hour course is based on the expectation that students will spend a minimum of 9 hours of study time per week in preparation for class (readings, papers, discussion sections, preparation for exams, etc.).