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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 111–120 of 151 papers

TitleStatusHype
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction—0
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models—0
KRED: Knowledge-Aware Document Representation for News RecommendationsCode0
Aligning Cross-Lingual Entities with Multi-Aspect InformationCode0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
Jointly Learning Entity and Relation Representations for Entity AlignmentCode0
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