Text Labeling

Definition

Text labeling is the process of assigning categories or tags to text, such as sentiment, topic, or named entities.

Purpose

The purpose is to transform raw text into structured data for supervised NLP tasks.

Importance

• Enables training of classification and extraction models.
• Quality affects model fairness and accuracy.
• Requires domain-specific expertise for specialized tasks.
• Labor-intensive at scale.

How It Works

1. Define label categories.
2. Segment text into units (sentences, documents).
3. Annotators assign labels.
4. Validate inter-annotator agreement.
5. Export labeled text for training.

Examples (Real World)

• Yelp reviews labeled for sentiment.
• Spam vs. ham email classification datasets.
• Legal text annotated for contract clauses.

References / Further Reading

• Pang & Lee. “Opinion Mining and Sentiment Analysis.”
• Bender & Friedman. “Data Statements for NLP.” ACL 2018.
• Hugging Face Datasets Documentation.