🤯 Did You Know (click to read)
BERT embeddings can be used to compare sentences for semantic similarity across multiple languages and domains.
BERT encodes text into embeddings that capture semantic meaning. Cosine similarity or other metrics can be applied to embeddings to determine closeness of meaning. This supports tasks such as duplicate question detection, plagiarism checking, and document clustering.
💥 Impact (click to read)
Text similarity analysis improves search relevance, content moderation, and knowledge organization, enabling automated detection of related content.
For users, BERT identifies related content accurately. The irony is that semantic similarity emerges statistically rather than through understanding of meaning.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
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