Entity Extraction Is Infrastructure Task, Not a Generative Task Large Language Models are powerful systems for language generation and reasoning. However, when they are used for entity extraction in enterprise environments, they introduce instability where reliability...
Why decompounding is a must-have non-optional requirement for e-commerce search, vector search, and RAG Search systems that work well in English, Spanish or French often collapse when they encounter German compounds or Korean eojeols. The issue is not ranking quality,...
Problem. There’s broad consensus today: LLMs are phenomenal personal productivity tools — they draft, summarize, and assist effortlessly.But there’s also growing recognition that they’re still not ready for enterprise-grade deployment. Why? Because enterprises need...
Rationale. NER tools are at the heart of how the scientific community is solving LLM issues using GraphRAG and NodeRAG architectures. LLMs need knowledge graphs to control hallucinations and make them more solid for enterprise-level use. And knowledge graphs are built...
As described in our previous post “Using Public Corpora to Build Your NER systems”, we are going to highlight areas where public datasets like OntoNotes or CoNLL can be improved. We will provide some tips on how to avoid these issues, whenever possible, using...
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