Semantic entity labeling
12 papers with code • 2 benchmarks • 2 datasets
- One of Form Understanding task (Word grouping, Semantic entity labeling, Entity linking)
- Classifying entities into one of four pre-defined categories: question, answer, header and, other.
cited from
G. Jaume, H. K. Ekenel, J. Thiran "FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents," 2019
Benchmarks
These leaderboards are used to track progress in Semantic entity labeling
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Libraries
Use these libraries to find Semantic entity labeling models and implementations
FUNSD
EC-FUNSD
Most implemented papers
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
jpwang/lilt
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ACL 2022
LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models.
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
microsoft/unilm
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In this paper, we propose \textbf{LayoutLMv3} to pre-train multimodal Transformers for Document AI with unified text and image masking.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems.
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis.
XDoc: Unified Pre-training for Cross-Format Document Understanding
microsoft/unilm
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The surge of pre-training has witnessed the rapid development of document understanding recently.
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing.
GeoLayoutLM: Geometric Pre-training for Visual Information Extraction
alibabaresearch/advancedliteratemachinery
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CVPR 2023
Additionally, novel relation heads, which are pre-trained by the geometric pre-training tasks and fine-tuned for RE, are elaborately designed to enrich and enhance the feature representation.
PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction
ZeningLin/PEneo
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However, simply concatenating SER and RE serially can lead to severe error propagation, and it fails to handle cases like multi-line entities in real scenarios.
