NLP Named Entity Recognition 5

Question 1

What is the role of a 'token classification head' when fine-tuning BERT for NER?

Accepted Answer

It applies a linear layer to each token's contextual representation to predict NER tags

Answer

A linear (dense) layer is placed on top of each token's BERT output embedding to project it into the NER label space, then trained with cross-entropy loss.

Question 2

Which evaluation strategy counts an entity as correct only if its entire span and type exactly match the gold annotation?

Accepted Answer

Exact (strict) match F1

Answer

Strict (exact) match F1 requires both span boundaries and entity type to be correct; partial overlap does not count as a true positive.

Question 3

What is 'data augmentation' commonly used for in low-resource NER?

Accepted Answer

Generating additional training examples by replacing entities with synonyms or back-translation

Answer

Entity-level substitution (swapping entity mentions with semantically similar ones) and back-translation are popular augmentation strategies to expand small NER training sets.

Question 4

In multi-task learning for NER, what is a common auxiliary task trained jointly with entity recognition?

Accepted Answer

Part-of-speech tagging or chunking

Answer

POS tagging and chunking share syntactic structure with NER, so training them jointly often improves entity recognition through shared representations.

Question 5

Which component of a pipeline-based information extraction system comes directly AFTER NER?

Accepted Answer

Relation extraction

Answer

Relation extraction identifies semantic relationships between entity pairs that NER has already identified, making it the natural downstream task.

NLP Practice Test

NLP Practice Test

NLP Named Entity Recognition 5