entity recognition
2 stories filed under entity recognition on Beyond Market Intelligence. The newest of them: “Tag spoken intent mid-sentence with Arc Tagger's real-time word-by-word analysis” and “Make LLM Reasoning Your NER Engine in Just a Few Steps”. Most AI assistants wait until you finish a sentence before they act. Building custom NER pipelines usually means wrestling with training data, annotation rules, and domain-specific tuning. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every entity recognition story on Beyond Market Intelligence, newest first.
Tag spoken intent mid-sentence with Arc Tagger's real-time word-by-word analysis
Most AI assistants wait until you finish a sentence before they act. That wastes time and misses intent that was obvious halfway through. Arc Tagger solves this by tagging intent, profanity, PII, and safety risks word by word as they arrive. In a demo, it flagged a bank impersonation scam mid-sentence. The approach feels practical and overdue. For voice pipelines, this kind of streaming awareness could transform how agents respond. For deeper context, our article on Apple's Siri Recaps explores related privacy tradeoffs.
Make LLM Reasoning Your NER Engine in Just a Few Steps
Building custom NER pipelines usually means wrestling with training data, annotation rules, and domain-specific tuning. That complexity becomes a barrier. You can skip most of it by letting an LLM's reasoning do the heavy lifting. In just a few steps, you define your entities, feed it text, and get structured results back. No model training required. It's a more direct path to standardization.