🤖 AI Summary
To address the challenge of jointly preserving semantic integrity and structural consistency in nested and overlapping named entity recognition (NER), this paper proposes a structure-aware decoding framework. The method leverages pretrained language model representations and integrates multi-granularity span composition with hierarchical decoding. Its core contributions are: (1) a novel collaborative mechanism between candidate span generation and structured attention, explicitly modeling entity boundaries, hierarchical nesting, and cross-entity dependencies; and (2) a joint optimization objective incorporating hierarchical structural constraints and semantic–structural consistency, combining classification loss with structure-consistency loss. Experimental results on ACE 2005 demonstrate significant F1-score improvements over prior work. The approach achieves superior precision, recall, and boundary localization for both nested and overlapping entities, and exhibits strong robustness on long sentences and scenarios with multiple co-occurring entities.
📝 Abstract
This paper proposes a structure-aware decoding method based on large language models to address the difficulty of traditional approaches in maintaining both semantic integrity and structural consistency in nested and overlapping entity extraction tasks. The method introduces a candidate span generation mechanism and structured attention modeling to achieve unified modeling of entity boundaries, hierarchical relationships, and cross-dependencies. The model first uses a pretrained language model to obtain context-aware semantic representations, then captures multi-granular entity span features through candidate representation combinations, and introduces hierarchical structural constraints during decoding to ensure consistency between semantics and structure. To enhance stability in complex scenarios, the model jointly optimizes classification loss and structural consistency loss, maintaining high recognition accuracy under multi-entity co-occurrence and long-sentence dependency conditions. Experiments conducted on the ACE 2005 dataset demonstrate significant improvements in Accuracy, Precision, Recall, and F1-Score, particularly in nested and overlapping entity recognition, where the model shows stronger boundary localization and structural modeling capability. This study verifies the effectiveness of structure-aware decoding in complex semantic extraction tasks, provides a new perspective for developing language models with hierarchical understanding, and establishes a methodological foundation for high-precision information extraction.