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Found 6 papers, 4 papers with code
DAME: Domain Adaptation for Matching Entities
We study the zero-shot learning case on the target domain, and demonstrate that our method learns the EM task and transfers knowledge to the target domain.
StruBERT: Structure-aware BERT for Table Search and Matching
In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table.
Neural ranking models for document retrieval
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.
Semantic Labeling Using a Deep Contextualized Language Model
Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking.
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks.
Leveraging Schema Labels to Enhance Dataset Search
We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels.
