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Named Entity Recognition Flashcards

7 cards from real NLP practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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

    Answer: BERT

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

  2. What is 'zero-shot NER'?

    Answer: Recognizing entity types not seen during training

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

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

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

    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.

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

    Answer: Biomedical / clinical text

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

  5. What is 'cross-lingual NER'?

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

    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.

  6. Why is NER particularly difficult on Twitter/social media data compared to newswire?

    Answer: Informal spelling, abbreviations, slang, and unconventional capitalization violate standard NER assumptions

    Social media text features inconsistent capitalization, abbreviations, hashtags, and misspellings that break lexical features and pre-trained models trained on formal text.

  7. Which span-based NER approach enumerates all candidate spans and classifies each one, enabling nested entity detection?

    Answer: Span classification models

    Span-based models score all possible text spans and classify each independently, naturally handling nested entities that sequential BIO tagging cannot represent.