mlsanityData to 14 Oct 2026

Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models

arXiv 2512.13980 ↗First posted 16 Dec 2025cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 16 Dec 2025.

Builds on

12 of 25 references.

References · 12

  1. 2023GPT-NER: Named Entity Recognition via Large Language Modelscited by 160
  2. 2022FiNER: Financial Numeric Entity Recognition for XBRL Taggingcited by 50
  3. 2021UniRE: A Unified Label Space for Entity Relation Extractioncited by 38
  4. 2025Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Modelscited by 9
  5. 2022An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognitioncited by 7
  6. 2022MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendationcited by 7
  7. 2025Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Datacited by 5
  8. 2023Gaussian Prior Reinforcement Learning for Nested Named Entity Recognitioncited by 3
  9. 2024In-Context Learning for Few-Shot Nested Named Entity Recognitioncited by 3
  10. 2025Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Modelscited by 3
  11. 2025Contrastive Knowledge Transfer and Robust Optimization for Secure Alignment of Large Language Modelscited by 3
  12. 2025Multi-Scale Feature Fusion and Graph Neural Network Integration for Text Classification with Large Language Modelscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimizationcited by 1
  2. 2026Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Spacecited by 0
  3. 2026Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Modelscited by 0