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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

Title	Date	Tasks	Status
KRED: Knowledge-Aware Document Representation for News Recommendations	Oct 25, 2019	Articlesdocument understanding	CodeCode Available
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy	Aug 11, 2020	Computational EfficiencyEntity Embeddings	CodeCode Available
DeepType: Multilingual Entity Linking by Neural Type System Evolution	Feb 3, 2018	Entity DisambiguationEntity Embeddings	CodeCode Available
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings	Sep 9, 2021	Entity EmbeddingsGraph Neural Network	CodeCode Available
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding	Jan 23, 2024	Entity AlignmentEntity Embeddings	CodeCode Available
Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense	Sep 6, 2021	Entity EmbeddingsKnowledge Graphs	CodeCode Available
Knowledge Hypergraphs: Prediction Beyond Binary Relations	Jun 1, 2019	Entity EmbeddingsKnowledge Graph Completion	CodeCode Available
DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph	Sep 14, 2023	Entity EmbeddingsEntity Linking	CodeCode Available
End-to-End Neural Entity Linking	Aug 23, 2018	Entity DisambiguationEntity Embeddings	CodeCode Available
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains	May 22, 2023	Entity EmbeddingsEntity Typing	CodeCode Available

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