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Column Type Annotation
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Column type annotation (CTA) refers to the task of predicting the semantic type of a table column and is a subtask of Table Annotation. The labels that are usually used in a CTA problem are semantic types from vocabularies like DBpedia, Schema.org or WikiData. Some examples are: Book, Country, LocalBusiness etc.
CTA can be either treated as a multi-class classification problem where a column is annotated by only one semantic type or as multi-label classification problem where a column can be annotated using multiple semantic types.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations
RACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graph
KGLink: A column type annotation method that combines knowledge graph and pre-trained language model
TableLlama: Towards Open Large Generalist Models for Tables
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
Adversarial Attacks on Tables with Entity Swap
CHORUS: Foundation Models for Unified Data Discovery and Exploration
Column Type Annotation using ChatGPT
TABBIE: Pretrained Representations of Tabular Data
Annotating Columns with Pre-trained Language Models
AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model
TURL: Table Understanding through Representation Learning
Sato: Contextual Semantic Type Detection in Tables
Learning Semantic Annotations for Tabular Data
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
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