nested ner
**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.