Papers with Code 아카이브
Semantic entity labeling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 23
- 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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
DocTr: Document Transformer for Structured Information Extraction in Documents
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding
GeoLayoutLM: Geometric Pre-training for Visual Information Extraction
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
XDoc: Unified Pre-training for Cross-Format Document Understanding
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.