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🤯 Did You Know (click to read)
BERT can distinguish 'Jaguar' the car from 'Jaguar' the animal using sentence context embeddings.
BERT uses bidirectional embeddings to compare entity mentions with potential candidates, leveraging context to resolve ambiguity. Fine-tuning on labeled datasets improves accuracy in linking mentions to the correct entity in knowledge bases or text.
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💥 Impact (click to read)
Entity disambiguation enhances search, information extraction, and knowledge graph construction, ensuring accurate representation of entities.
For users, BERT correctly interprets ambiguous mentions. The irony is that this disambiguation arises from statistical context matching rather than true understanding.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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