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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 91–100 of 151 papers

TitleStatusHype
Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis—0
A Joint Training Framework for Open-World Knowledge Graph Embeddings—0
“Politeness, you simpleton!” retorted [MASK]: Masked prediction of literary characters—0
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
Capturing Knowledge of Emerging Entities From Extended Search SnippetsCode0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
A Mixture-of-Experts Model for Learning Multi-Facet Entity EmbeddingsCode0
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