Data annotation is the process of adding tags to content like text, photos, and videos so that machine learning models can find them and use them to make predictions.
When we label parts of the data, ML models know precisely what they’re going to do with it and keep that information so that they can automatically use the available information and what they already know to make decisions.
This process has tremendous value, especially considering how much-unstructured data is waiting to be analyzed. That said, it’s essential to know why data annotation matters so that you can maximize the benefits of this worthwhile procedure.
You might also label or add metadata to language data to add helpful information about it, and this process is called text annotation.