Annotation

In machine learning, annotation transforms raw data such as text, images, audio, or video into labeled examples that models can learn from. Depending on the task, annotations may identify objects in images, classify text, mark entities, transcribe speech, or assign sentiment labels. Annotations can be created manually by humans, automatically using software, or through a combination of both approaches.

High-quality annotations provide models with reliable examples of what they are expected to recognize or predict. Incorrect or inconsistent labels can negatively affect model accuracy and introduce bias. Well-managed annotation therefore plays an important role in building reliable supervised learning systems.