Annotation Label
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An Annotation Label is a annotation item element used in Data Annotation to categorize or identify objects, texts, or other data artifacts.
- Context:
- It can (typically) be used in Machine Learning tasks to provide ground truth data for training algorithms.
- It can (often) be part of a Labeling Task where human annotators or automated systems assign labels based on predefined categories.
- It can range from identifying simple attributes such as Color or Size to more complex labels like Emotional State or Semantic Role.
- It can be integral to Supervised Learning, where labeled data informs the learning process.
- It can serve as a crucial tool in various applications such as Image Recognition, Text Categorization, and Voice Identification.
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- Example(s):
- an Image Annotation Label that identifies the main subject in a photograph, such as 'cat' or 'street sign'.
- a Text Annotation Label used in sentiment analysis to indicate whether a statement is positive, negative, or neutral.
- a Voice Annotation Label in speech recognition systems that recognize spoken words or phrases.
- a Category Annotation Tag in content management systems that helps sort and retrieve content based on similar themes.
- a Demographic Annotation Label in survey data that denotes respondent characteristics such as age, gender, or income level.
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- Counter-Example(s):
- Raw Data, which has not been processed or labeled.
- Unlabeled Dataset, which is used in Unsupervised Learning where no labels are provided.
- See: Data Annotation, Categorization, Tag Item.