mlsanityData to 14 Oct 2026

Entity Relation Extraction as Dependency Parsing in Visually Rich Documents

arXiv 2110.09915 ↗First posted 19 Oct 2021cs.CL cs.AI cs.LGcited by 23

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Abstract (arXiv)

Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead. We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders. The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset. As for the real-world application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 23 of its 27 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

23 of 27 references.

References · 23

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11 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 8; 2025: 2; 2026: 1.

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  1. 2023GeoLayoutLM: Geometric Pre-training for Visual Information Extractioncited by 40
  2. 2023Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answeringcited by 24
  3. 2023Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Predictioncited by 23
  4. 2024Deep Learning based Visually Rich Document Content Understanding: A Surveycited by 21
  5. 2024Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysiscited by 15
  6. 2024Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understandingcited by 12
  7. 2022ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understandingcited by 9
  8. 2023A Question-Answering Approach to Key Value Pair Extraction from Form-like Document Imagescited by 9
  9. 2023Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understandingcited by 6
  10. 2024Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Reviewcited by 6
  11. 2023PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layoutscited by 4
  12. 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligencecited by 4
11 more
  1. 2023Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Documentcited by 2
  2. 2024Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidencecited by 2
  3. 2025FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Modelscited by 2
  4. 2022LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documentscited by 1
  5. 2022DoSA : A System to Accelerate Annotations on Business Documents with Human-in-the-Loopcited by 1
  6. 2024A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documentscited by 1
  7. 2023RE2: Region-Aware Relation Extraction from Visually Rich Documentscited by 0
  8. 2023Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documentscited by 0
  9. 2024UniVIE: A Unified Label Space Approach to Visual Information Extraction from Form-like Documentscited by 0
  10. 2024Thai Universal Dependency Treebankcited by 0
  11. 2025Hierarchical Document Parsing via Large Margin Feature Matching and Heuristicscited by 0