all AI news
What is the most important/painful step in NLP Data Management?
I am doing research for a project regarding NLP Data Management.
My team and me identified the following five overarching building blocks in machine learning data management.
Now specifically in regard to NLP. Which one of these steps do you regard as most important / most painful?
I’d be really happy for any (gladly very specific) examples you encounter in your work or research.
Thanks in advance!
More from reddit.com / Natural Language Processing
Have there been fruitful attempts to decode sentence embeddings? 2 days, 18 hours ago | reddit.com
Machine Learning Researcher - Saalfeld Lab
@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia
Project Director, Machine Learning in US Health
@ ideas42.org | Remote, US
Data Science Intern
@ NannyML | Remote
Machine Learning Engineer NLP/Speech
@ Play.ht | Remote
Research Scientist, 3D Reconstruction
@ Yembo | Remote, US
Clinical Assistant or Associate Professor of Management Science and Systems
@ University at Buffalo | Buffalo, NY