Semantic entity labeling
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Benchmarks
Most implemented
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
Papers
Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding
Modeling and leveraging layout reading order in visually-rich documents (VrDs) is critical in document intelligence as it captures the rich structure semantics within documents. Previous works typically formulated layout…
document understandingEntity LinkingKey Information ExtractionReading Order Detection+2Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
Recently developed pre-trained text-and-layout models (PTLMs) have shown remarkable success in multiple information extraction tasks on visually-rich documents. However, the prevailing evaluation pipeline may not be suff…
Entity LinkingSemantic entity labelingPEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction
Document pair extraction aims to identify key and value entities as well as their relationships from visually-rich documents. Most existing methods divide it into two separate tasks: semantic entity recognition (SER) and…
Key Information ExtractionKey-value Pair ExtractionRelation ExtractionSemantic entity labelingReading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
Recent advances in multimodal pre-trained models have significantly improved information extraction from visually-rich documents (VrDs), in which named entity recognition (NER) is treated as a sequence-labeling task of p…
Entity LinkingKey Information ExtractionKey-value Pair Extractionnamed-entity-recognition+9DocTr: Document Transformer for Structured Information Extraction in Documents
We present a new formulation for structured information extraction (SIE) from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based formulations, which are either overly relia…
Entity LinkingSemantic entity labelingLayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding
Visually-rich Document Understanding (VrDU) has attracted much research attention over the past years. Pre-trained models on a large number of document images with transformer-based backbones have led to significant perf…
document-image-classificationDocument Image Classificationdocument understandingimage-classification+9