Manual annotation involves human annotators labeling data, ensuring high accuracy and quality, especially for complex tasks.
Semi-automatic annotation uses algorithms to pre-label data, with humans refining the annotations, balancing efficiency and accuracy.
Automatic annotation relies on algorithms to label data without human intervention, ideal for large datasets requiring quick turnaround."
Crowdsourced annotation involves a distributed group of people labeling data, harnessing the power of the crowd to scale efforts.
Synthetic annotation generates annotated data using simulations or synthetic data, useful for training models when real data is scarce.
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