NLP Named Entity Recognition 4

Question 1

Which pre-trained model popularized the use of contextual embeddings for NER and achieved state-of-the-art results on CoNLL-2003?

Accepted Answer

BERT

Answer

BERT's contextual embeddings, fine-tuned with a token classification head, set new state-of-the-art records on CoNLL-2003 NER in 2018.

Question 2

What is 'zero-shot NER'?

Accepted Answer

Recognizing entity types not seen during training

Answer

Zero-shot NER recognizes entity types absent from the training set, typically by leveraging type descriptions or prompting large language models.

Question 3

In few-shot NER, what is a 'support set'?

Accepted Answer

A small set of labeled examples provided at inference time for new entity types

Answer

In few-shot NER, the support set contains a handful of labeled examples per new entity type that the model uses to generalize at inference.

Question 4

Which domain is known for particularly challenging NER due to highly specialized terminology and non-standard abbreviations?

Accepted Answer

Biomedical / clinical text

Answer

Biomedical NER must recognize genes, proteins, diseases, and drugs with complex nomenclature, making it harder than general-domain NER.

Question 5

What is 'cross-lingual NER'?

Accepted Answer

Training on one language and transferring the model to recognize entities in another

Answer

Cross-lingual NER trains on a high-resource language and transfers to a low-resource target language, leveraging multilingual embeddings like mBERT or XLM-R.

NLP Practice Test

NLP Practice Test

NLP Named Entity Recognition 4