Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …

This paper introduces a conceptually simple, scalable, and highly effective BERT-based
entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We …

Natural language understanding comprises a wide range of diverse tasks such as textual
entailment, question answering, semantic similarity assessment, and document …

A survey of computational methods for understanding, generating, and manipulating human
language, which offers a synthesis of classical representations and algorithms with …

Entity Linking (EL) is an essential task for semantic text understanding and information
extraction. Popular methods separately address the Mention Detection (MD) and Entity …

We propose a novel deep learning model for joint document-level entity disambiguation,
which leverages learned neural representations. Key components are entity embeddings, a …

We show that it is feasible to perform entity linking by training a dual encoder (two-tower)
model that encodes mentions and entities in the same dense vector space, where candidate …

Much effort has been devoted to evaluate whether multi-task learning can be leveraged to
learn rich representations that can be used in various Natural Language Processing (NLP) …

The large number of potential applications from bridging web data with knowledge bases
have led to an increase in the entity linking research. Entity linking is the task to link entity …