mlsanityData to 14 Oct 2026

Hypergraph based Understanding for Document Semantic Entity Recognition

arXiv 2407.06904 ↗First posted 9 Jul 2024cs.AIcited by 4

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Abstract (arXiv)

Semantic entity recognition is an important task in the field of visually-rich document understanding. It distinguishes the semantic types of text by analyzing the position relationship between text nodes and the relation between text content. The existing document understanding models mainly focus on entity categories while ignoring the extraction of entity boundaries. We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time. It can conduct a more detailed analysis of the document text representation analyzed by the upstream model and achieves a better performance of semantic information. We apply this method on the basis of GraphLayoutLM to construct a new semantic entity recognition model HGALayoutLM. Our experiment results on FUNSD, CORD, XFUND and SROIE show that our method can effectively improve the performance of semantic entity recognition tasks based on the original model. The results of HGALayoutLM on FUNSD and XFUND reach the new state-of-the-art results.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

14 of 29 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  5. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  6. 2023CogAgent: A Visual Language Model for GUI Agentscited by 704
  7. 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 408
  8. 2021LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understandingcited by 103
  9. 2024LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understandingcited by 95
  10. 2022ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understandingcited by 67
  11. 2022FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extractioncited by 64
  12. 2021ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extractioncited by 22
2 more
  1. 2022Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognitioncited by 15
  2. 2022ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understandingcited by 9

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understandingcited by 23
  2. 2025HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devicescited by 15
  3. 2024Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understandingcited by 12
  4. 2025MGA-VQA: Secure and Interpretable Graph-Augmented Visual Question Answering with Memory-Guided Protection Against Unauthorized Knowledge Usecited by 1