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GraphIE: A Graph-Based Framework for Information Extraction

arXiv 1810.13083 ↗First posted 31 Oct 2018cs.CLcited by 45

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Abstract (arXiv)

Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks --- namely textual, social media and visual information extraction --- shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2018.

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Citing papers per year: 2021: 16; 2022: 8; 2023: 13; 2024: 5; 2025: 1; 2026: 2.

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  1. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  2. 2022LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understandingcited by 107
  3. 2021StrucTexT: Structured Text Understanding with Multi-Modal Transformerscited by 90
  4. 2021Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solutioncited by 64
  5. 2022FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extractioncited by 64
  6. 2023Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solutioncited by 54
  7. 2023GeoLayoutLM: Geometric Pre-training for Visual Information Extractioncited by 40
  8. 2021ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documentscited by 29
  9. 2023Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answeringcited by 24
  10. 2021ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extractioncited by 22
  11. 2021MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extractioncited by 18
  12. 2022Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understandingcited by 18
33 more
  1. 2023Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentrationcited by 18
  2. 2021Cost-effective End-to-end Information Extraction for Semi-structured Document Imagescited by 14
  3. 2023Towards reducing hallucination in extracting information from financial reports using Large Language Modelscited by 11
  4. 2022ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understandingcited by 9
  5. 2023Universal Graph Continual Learningcited by 9
  6. 2021Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequencescited by 8
  7. 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Modelscited by 7
  8. 2021A Span Extraction Approach for Information Extraction on Visually-Rich Documentscited by 6
  9. 2022TRIE++: Towards End-to-End Information Extraction from Visually Rich Documentscited by 6
  10. 2024DocGraphLM: Documental Graph Language Model for Information Extractioncited by 6
  11. 2024SAIL: Sample-Centric In-Context Learning for Document Information Extractioncited by 6
  12. 2021Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phonescited by 5
  13. 2021Larger-Context Tagging: When and Why Does It Work?cited by 4
  14. 2023A New Task and Dataset on Detecting Attacks on Human Rights Defenderscited by 4
  15. 2024Hierarchical Multimodal Pre-training for Visually Rich Webpage Understandingcited by 4
  16. 2021One-shot Key Information Extraction from Document with Deep Partial Graph Matchingcited by 3
  17. 2022Summarization with Graphical Elementscited by 2
  18. 2022HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence Systemcited by 2
  19. 2023Seq-HyGAN: Sequence Classification via Hypergraph Attention Networkcited by 2
  20. 2024GraphRevisedIE: Multimodal Information Extraction with Graph-Revised Networkcited by 2
  21. 2023ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamesecited by 1
  22. 2024Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentencescited by 1
  23. 2021Semantic Parsing to Manipulate Relational Database For a Management Systemcited by 0
  24. 2021Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representationscited by 0
  25. 2021Exploiting Global Contextual Information for Document-level Named Entity Recognitioncited by 0
  26. 2021Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM treecited by 0
  27. 2021KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformerscited by 0
  28. 2023Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks?cited by 0
  29. 2023RE2: Region-Aware Relation Extraction from Visually Rich Documentscited by 0
  30. 2023Topic-Guided Self-Introduction Generation for Social Media Userscited by 0
  31. 2025TASER: Table Agents for Schema-guided Extraction and Recommendationcited by 0
  32. 2026From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarizationcited by 0
  33. 2026Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Modelscited by 0