Guide-linked quiz · Medium Level

Entity Linking and Disambiguation Quiz

Test mention detection, context, candidate generation, bi-encoder retrieval, cross-encoder reranking, NIL outputs, and document-level coherence.

Question 1 of 8

What does entity linking connect a text mention to?

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Answers and explanations appear as you take the quiz.

  1. What does entity linking connect a text mention to?
    • A random web page
    • A new training run
    • A spelling correction
    • A unique entry in a knowledge base
  2. In the phrase 'the company Apple released a phone', what is the disambiguation task?
    • Deciding Apple means the fruit, not the company
    • Deciding Apple means the tech company, not the fruit
    • Translating Apple into another language
    • Counting the letters in Apple
  3. What is the purpose of the candidate generation stage?
    • To write a new article
    • To delete ambiguous words
    • To retrieve a shortlist of possible matching entities
    • To translate the text
  4. What role does a bi-encoder play in modern entity linking?
    • Fast retrieval via separate embeddings for mention and entities
    • Slow manual labeling of every mention
    • Generating images of entities
    • Encrypting the knowledge base
  5. What does a NIL class represent in entity linking?
    • The most popular entity
    • A mention with no matching knowledge-base entry
    • A duplicate entity
    • A training error
  6. How can document-level coherence help an entity-linking system?
    • By guaranteeing that every mention has the same entity identifier
    • By proving every document discusses only one topic
    • By removing the need to evaluate individual mention contexts
    • By considering whether candidate assignments fit the document context and one another
  7. After fast candidate retrieval, what information does a cross-encoder jointly read for entity-linking reranking?
    • The mention's context and a candidate entity's description
    • Only the entity's numeric identifier with no text
    • Only the two input filenames
    • Only the number of images in the knowledge base
  8. If an entity-linking pipeline never proposes the span 'Apple' for analysis, which initial stage has missed that mention?
    • The final candidate reranker only
    • The image-compression stage
    • Mention detection
    • The knowledge base's output filename formatter

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