Entity Embeddings
Related Papers
Deep Joint Entity Disambiguation with Local Neural Attention
AbstractWe propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, …...
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
AbstractEntity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in …...
End-to-End Neural Entity Linking
AbstractEntity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without …...
Learning Relational Representations by Analogy using Hierarchical Siamese Networks
AbstractWe address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of …...
Improving Entity Linking through Semantic Reinforced Entity Embeddings
AbstractEntity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned …...
Improving Entity Linking by Encoding Type Information into Entity Embeddings
AbstractEntity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities inthe Knowledge Base (KB) in which entity embeddings play a vital and …...