When it comes to annotating data for academic purposes, there are specific industry standards that are commonly used. However, when it comes to the commercial sector, building a solution that relies on machine learning requires different data annotation standards. To build a strong solution that can understand and mimic humans, high-quality, human-annotated training data is key.
Interested in learning more? Watch our free on-demand webinar, part of AI Trends’ webinar series.
In this webinar, James Lyle, Director of Custom Linguistic Solutions, discusses:
- The pros and cons of licensable public data vs. building your own datasets
- Choices and tradeoffs in the level of effort you invest in acquiring and labeling data
- Why curated crowds yield higher quality data for your machine learning
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