{ "lesson": "25-entity-linking", "title": "Entity Linking & Disambiguation", "questions": [ { "stage": "pre", "question": "What does entity linking add on top of NER?", "options": [ "Translations", "Sentiment polarity", "Part-of-speech tags", "It maps each detected mention to a unique entry in a knowledge base (Wikidata, Wikipedia, or a domain KB)" ], "correct": 3, "explanation": "EL turns a mention into a canonical KB id, disambiguating between same-name entities." }, { "stage": "pre", "question": "What are the two main subtasks in entity linking?", "options": [ "Tokenization and parsing", "POS tagging and lemmatization", "Embedding and clustering", "Candidate generation (shortlist of plausible KB entries) and disambiguation (pick the right one given context)" ], "correct": 3, "explanation": "EL decomposes into proposing candidates then ranking them by contextual fit." }, { "stage": "check", "question": "Why must mention recall be reported alongside disambiguation accuracy?", "options": [ "Mention recall is mandatory by GDPR", "Recall replaces precision", "Disambiguation cannot recover from missing candidates; the pipeline is bounded by candidate-generation recall", "Recall is required by spaCy" ], "correct": 2, "explanation": "If candidates miss the gold entity, no disambiguator can fix it; recall floors pipeline quality." }, { "stage": "check", "question": "How does GENRE perform entity linking?", "options": [ "Decodes the entity's canonical name token-by-token under constrained decoding over a trie of valid KB ids", "By BM25", "By computing TF-IDF", "By PageRank" ], "correct": 0, "explanation": "GENRE generates the canonical KB name with constrained decoding to guarantee a valid id." }, { "stage": "check", "question": "What is NIL handling in entity linking?", "options": [ "Predicting a 'not in KB' label when no candidate is a real match (emerging entities, obscure people)", "Returning the entire candidate list", "Lowercasing the input", "Skipping every mention" ], "correct": 0, "explanation": "NIL prediction prevents guessing wrong KB ids for entities the KB does not cover." }, { "stage": "post", "question": "Why does popularity bias hurt entity linking in specialized domains?", "options": [ "Models trained on web data over-predict frequent entities (e.g. basketball Jordan over the ML researcher Michael I. Jordan)", "It biases toward older entities only", "Popularity removes NIL", "Popularity helps recall" ], "correct": 0, "explanation": "Popularity priors skew predictions away from less-common, domain-specific name-clashes." }, { "stage": "post", "question": "What is a safe LLM-EL pattern in 2026?", "options": [ "Provide a candidate list and use constrained JSON output that the LLM can only choose from valid KB ids", "Just prompt 'find the entity'", "Free-form generation", "Skip candidate generation" ], "correct": 0, "explanation": "Constraining the LLM to a valid candidate list prevents made-up KB ids and keeps output queryable." }, { "stage": "post", "question": "Why must NER mention boundaries be exact for entity linking to work?", "options": [ "Boundary errors propagate: 'Bank of America' clipped to 'Bank' surfaces wrong candidates and tanks EL recall", "Boundary precision only affects NER metrics, not entity-linking candidate quality", "Boundaries change tokenization", "Boundaries break Wikipedia lookups" ], "correct": 0, "explanation": "Mis-bounded mentions retrieve the wrong alias set, propagating errors into disambiguation." } ] }