🤯 Did You Know (click to read)
BERT can extract entities and their relations from raw text, creating structured data for AI applications.
Using contextual embeddings and transformer self-attention, BERT can extract structured information from unstructured text. Fine-tuning on datasets for information extraction allows it to identify entities, relationships, and attributes, supporting applications such as knowledge graph creation, automated tagging, and content summarization.
💥 Impact (click to read)
Information extraction accelerates data processing and insight generation for business intelligence, research, and content analysis.
For users, BERT provides organized information from complex text. The irony is that structure emerges statistically rather than through comprehension.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
💬 Comments