Named Entity Recognition Then and Now
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Named entity recognition is span labelling: find the character offsets of every person, organisation and place in a text and tag each one. That framing — offsets, not a list — is where most comparisons between classical NER and LLM extraction quietly go wrong. The task, precisely A classical NER system consumes a tokenised sentence and emits one tag per token in a scheme such as BIO: B-ORG for the first token of an organisation, I-ORG for subsequent ones, O for everything else. Two properties…
1Key Takeaways
- Named entity recognition is span labelling: find the character offsets of every person, organisation and place in a text and tag each one.
- That framing — offsets, not a list — is where most comparisons between classical NER and LLM extraction quietly go wrong.
- The task, precisely A classical NER system consumes a tokenised sentence and emits one tag per token in a scheme such as BIO: B-ORG for the first token of an organisation, I-ORG for subsequent ones, O for everything else.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that named entity recognition is span labelling: find the character offsets of every person, organisation and place in a text and tag each one.
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