FAQ
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Data Labeling
How Does Text Annotation Work?
Text annotation works by adding labels or structural metadata to written language so a model can learn from it. The process typically starts with guidelines that define how each type of text should be labeled, then annotators review the text and apply labels accordingly.
Components of Text Annotation
- Classification: Sorting text into predefined categories, such as topic or intent
- Sentiment labeling: Identifying whether text expresses positive, negative or neutral tone
- Entity tagging: Marking names, places, dates or other specific terms within a passage
- Intent and dialogue labeling: Identifying what a speaker or user is trying to accomplish, often used for chatbots and conversational AI
- Relevance and ranking: Judging how well a piece of text answers a query or matches a prompt
- Content moderation labeling: Flagging text for policy violations, toxicity or safety concerns
Because language contains ambiguity, slang and edge cases, consistency and cultural nuance matter as much as speed. At BUNCH, we offer trained in-house teams, double-pass annotation and ongoing QA audits to keep quality aligned.
Guidelines are refined as new edge cases surface, whether the project is a one-time dataset or an ongoing pipeline.
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