Nested NER handles entities within entities — recognizing that "Bank of America" contains both an organization ("Bank of America") and a location ("America"), or that "New York University Medical Center" has nested organization and location entities.

What Is Nested NER?

• Definition: Recognize overlapping or nested entity mentions.
• Example: "Bank of [America]LOC" is also "[Bank of America]ORG".
• Challenge: Traditional NER assumes non-overlapping entities.

Nested Entity Examples

Organization + Location: "Bank of [America]LOC" → "[Bank of America]ORG". Person + Organization: "[Michael]PER [Jordan]PER" → "[Michael Jordan]PER". Product + Organization: "[Microsoft]ORG [Windows]PRODUCT" → "[Microsoft Windows]PRODUCT". Location Hierarchy: "[New York]CITY [City]" → "[New York City]CITY".

Why Nested NER?

• Completeness: Capture all entity mentions, not just outermost.
• Precision: Distinguish "America" (location) from "Bank of America" (organization).
• Knowledge Extraction: Build richer knowledge graphs.
• Domain-Specific: Medical, legal texts have complex nested entities.

Approaches

Layered Tagging: Multiple NER passes for different nesting levels. Span-Based: Enumerate all possible spans, classify each. Hypergraph: Model nested structure as hypergraph. Transition-Based: Parse entities like syntactic parsing. Neural Models: Span-based BERT models, nested attention.

Challenges: Exponential span candidates, ambiguous boundaries, rare nested patterns, computational cost.

Applications: Biomedical NER (nested gene/protein names), legal documents, news analysis, knowledge base construction.

Tools: Nested NER models in research, spaCy with custom components, specialized biomedical NER systems.