The hybrid method with LLM-augmented data achieves F1 improvements of 7-24 points over baselines on five Vietnamese domain datasets.
Neural Architectures for Named Entity Recognition
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MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
citing papers explorer
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A Hybrid Method for Low-Resource Named Entity Recognition
The hybrid method with LLM-augmented data achieves F1 improvements of 7-24 points over baselines on five Vietnamese domain datasets.
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A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
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TabEmb: Joint Semantic-Structure Embedding for Table Annotation
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.