A dynamic parsing and relationship inference model for entity features that incorporates gating units and pooling strategies to dynamically update hidden states is proposed that outperforms the baseline model Adaptive Thresholding and Localized Context Pooling, particularly in extracting potential relationships across sentence–boundary entity pairs.
A three-stage framework made up of neighborhood-aware retrieval, dual reranking, and score fusion that achieves the state of the art on average across five widely-used benchmarks and remains efficient at inference.
Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated...
Cross-domain Named Entity Recognition (CD-NER) aims to transfer the rich knowledge in the source domain to the target domain. Recent studies adopting decomposition or generation paradigms have achieved significant performance improvements, demonstrating high accuracy in entity span detection. However, during entity typ...
Jing-Yu Wang, Shijie Wu, Fu-Sheng Jin· Proceedings of the Thirty-Fi...· 0 citations
This work introduces an ontology-driven framework to quantify and enforce structural consistency in DocRE datasets and reveals substantial structural noise in DocRED distant and demonstrates that such inconsistencies propagate to model predictions.
Laura Menotti, Stefano Marchesin, Gianmaria Silvello· 0 citations
Experimental results on benchmark datasets demonstrate that the NER approach significantly improves clustering performance, highlighting the value of entity-aware smoothing over standard pretrained representations.
Imed Keraghel, Mohamed Nadif· Annual International ACM SIG...· 0 citations
This analysis shows that, in new sentences, the contextual associations of tokens representing old entity types exhibit a significantly stronger bias towards new entity types compared to their contexts in old sentences, which intensifies the degradation of old knowledge while promoting the overfitting of new knowledge.
Du-Zhen Zhang, Yahan Yu, Xiuyi Chen et al.· IEEE Transactions on Artific...· 0 citations
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