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#named-entity-recognition News & Analysis

5 articles tagged with #named-entity-recognition. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles
AINeutralarXiv – CS AI · 2d ago6/10
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Specialty-Specific Medical Language Model for Immune-Mediated Diseases

Researchers developed a specialized Named Entity Recognition model for identifying disease-related clinical entities in immunology and infectious disease texts, achieving 0.89 F1 score through transformer-based architecture with clinical embeddings. The model outperforms general-purpose NLP systems and LLMs in extracting granular biomedical concepts from unstructured medical narratives, enabling improved cohort identification and clinical decision support.

AINeutralarXiv – CS AI · 4d ago6/10
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LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation

Researchers have extended LELA, an LLM-based entity linking framework, into a practical Python library that combines zero-shot Named Entity Recognition with entity disambiguation. The end-to-end pipeline addresses limitations in existing approaches by offering domain-agnostic capabilities and demonstrating robust performance across diverse entity linking tasks, making it more applicable to real-world usage scenarios.

AIBullisharXiv – CS AI · Apr 206/10
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DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

Researchers introduce DiZiNER, a framework that improves zero-shot named entity recognition by simulating human annotation disagreement processes using multiple LLMs. The approach achieves state-of-the-art results on 14 of 18 benchmarks, closing the performance gap between zero-shot and supervised systems by over 11 percentage points.

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AIBullisharXiv – CS AI · Mar 26/1012
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TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining

Researchers developed TRIZ-RAGNER, a retrieval-augmented large language model framework that improves patent analysis and systematic innovation by extracting technical contradictions from patent documents. The system achieved 84.2% F1-score accuracy, outperforming existing methods by 7.3 percentage points through better integration of domain-specific knowledge.