NLP Named Entity Recognition 3

Question 1

Which character-level feature is commonly added to NER models to help identify proper nouns?

Accepted Answer

Capitalization features

Answer

Capitalization is a strong surface cue for proper nouns (entities) in English and many other languages, so it is a standard NER feature.

Question 2

In transformer-based NER, why is subword tokenization (e.g., WordPiece) potentially problematic?

Accepted Answer

Entity boundaries may fall within a subword split, complicating label alignment

Answer

Subword tokenization can split a single word into multiple tokens, requiring strategies to align word-level NER labels with subword tokens.

Question 3

What is 'gazetteer' in the context of NER?

Accepted Answer

A list of known entities used as a lookup feature

Answer

A gazetteer is a dictionary of known entity names (e.g., cities, companies) whose presence in text provides strong evidence for NER models.

Question 4

Which NER approach does NOT require annotated training data?

Accepted Answer

Dictionary-based / rule-based NER

Answer

Dictionary-based and rule-based NER systems rely on predefined lists and patterns rather than labeled examples, requiring no annotated corpus.

Question 5

What is 'entity linking' (also called entity disambiguation) in relation to NER?

Accepted Answer

Mapping a recognized entity mention to a specific entry in a knowledge base

Answer

Entity linking maps surface mentions (e.g., 'Apple') identified by NER to canonical knowledge base entries (e.g., Apple Inc. vs. the fruit).

NLP Practice Test