Back to the journalText Annotation for NLP: NER, Intent and Sentiment Guide
Short answer: text annotation means adding labels to written language so NLP models and large language models can learn from it. The most common tasks are named entity recognition (marking names, places, products, dates in text), intent and topic classification, sentiment labeling, relation labeling between entities, and document-level tags. Each needs a clear rulebook because language is ambiguous.
Main text annotation tasks
| Task | What gets labeled | Example |
|---|---|---|
| Named entity recognition (NER) | Spans of text with a type | "[Acme Corp]ORG opened an office in [Chicago]LOC" |
| Intent classification | The goal of a message | "Where is my order?" → order_status |
| Sentiment | Positive, negative, neutral (or a scale) | "The setup was painful" → negative |
| Relation extraction | Links between entities | Person works_for Organization |
| Document classification | Category of a whole document | Invoice, contract, resume |
| LLM evaluation | Quality of model answers | Accuracy, helpfulness, safety scores |
Named entity recognition in practice
NER looks simple until edge cases appear. Rules should cover:
- Span boundaries: include titles ("Dr.") and suffixes ("Inc.") or not?
- Nested entities: "Bank of America Plaza" is a building that contains an organization name.
- Ambiguity: "Jordan" as a person, a country or a brand, decided by context.
- Dates and numbers: which formats count, and whether ranges are one span.
- Typos and casing in user-written text.
Intent and sentiment edge cases
- Multiple intents in one message ("cancel my order and update my address").
- Sarcasm ("great, another delay").
- Mixed sentiment ("the product is good but support is slow").
- Out-of-scope messages that fit no intent: always provide an "other" or "none" class.
Quality checks for text data
- Agreement on spans: exact match vs partial overlap, decided in advance.
- Kappa for classification tasks to correct for chance agreement.
- Confusion review: which classes annotators mix up most often; those definitions need work.
- Gold examples in every batch.
- Language and domain match: legal, medical and multilingual text need annotators with that background.
Text annotation for LLMs
LLM projects add tasks such as writing ideal answers, ranking model outputs, and checking facts. These depend even more on rubrics. See RLHF data annotation.
Privacy and sensitive text
Customer messages, medical notes and documents often contain personal data. Agree on redaction rules, access controls and deletion before work starts.
Have a dataset in mind?
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