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Entity Embeddings

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.

Papers

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Showing 101–110 of 151 papers

TitleStatusHype
Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models—0
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
Entity Embeddings of Categorical VariablesCode0
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
Merge and Label: A novel neural network architecture for nested NERCode0
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation ExtractionCode0
Ered: Enhanced Text Representations with Entities and DescriptionsCode0
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