NER Annotation: How to Build High-Quality Named Entity Recognition Datasets
Named Entity Recognition is one of the most critical NLP tasks — and its quality depends entirely on the annotation.
What Is NER and Why Does It Matter?
NER identifies and classifies named entities in text — people, organisations, locations, dates, monetary values, and more. It's foundational for information extraction, question answering, and document understanding systems.
Common Entity Types: PERSON, ORG, LOC, DATE, AMOUNT, PRODUCT
Each entity type requires precise definition in your annotation guidelines to ensure consistency across annotators.
Designing NER Annotation Guidelines: Boundary Rules, Nested Entities, Ambiguity
The most common source of annotation error is ambiguity in guidelines — particularly around entity boundaries and nested entities. Clear examples and decision trees are essential.
Multilingual NER Challenges: Code-Switching, Script Changes, Transliteration
Multilingual NER is significantly harder than single-language annotation. Code-switching (e.g., Hindi-English sentences) and transliteration require specialised annotator training.
Measuring Quality: IAA Scores and Adjudication Workflows
Cohen's Kappa and F1-based IAA scores measure annotator agreement on entity spans and types. Disagreements must be adjudicated by senior reviewers, not ignored.
Delivery Formats: CoNLL vs JSONL vs Custom Schema
- CoNLL format: Industry standard for sequence labeling tasks
- JSONL: Flexible, machine-readable, easy to process programmatically
- Custom schema: Sometimes required for complex nested entity structures
Case Study: eQOURSE NER Annotation for a FinTech Chatbot
We annotated 500,000+ customer service utterances with 12 entity types for a FinTech NLU system, achieving 0.94 Cohen's Kappa.
Build your NER dataset with eQOURSE