🤯 Did You Know (click to read)
BERT can infer the correct meaning of words with multiple senses by analyzing both preceding and following context simultaneously.
BERT’s bidirectional encoding allows it to interpret words whose meaning depends on context, such as 'bank' in 'river bank' versus 'bank account.' Self-attention across layers enables the model to weigh surrounding tokens, capturing semantic nuances. This capability improves performance in tasks like word sense disambiguation, question answering, and language understanding.
💥 Impact (click to read)
Polysemy resolution enhances NLP applications by reducing ambiguity, improving search relevance, and enabling accurate language interpretation.
For users, BERT seems to understand multiple word meanings. The irony is that these interpretations are derived statistically, not cognitively.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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