Named entity extraction for knowledge graphs: A literature overview
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 …
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …
Entity linking meets deep learning: Techniques and solutions
Entity linking (EL) is the process of linking entity mentions appearing in web text with their
corresponding entities in a knowledge base. EL plays an important role in the fields of …
corresponding entities in a knowledge base. EL plays an important role in the fields of …
Autoregressive entity retrieval
Entities are at the center of how we represent and aggregate knowledge. For instance,
Encyclopedias such as Wikipedia are structured by entities (eg, one per Wikipedia article) …
Encyclopedias such as Wikipedia are structured by entities (eg, one per Wikipedia article) …
Cm3: A causal masked multimodal model of the internet
We introduce CM3, a family of causally masked generative models trained over a large
corpus of structured multi-modal documents that can contain both text and image tokens …
corpus of structured multi-modal documents that can contain both text and image tokens …
End-to-end neural entity linking
Entity Linking (EL) is an essential task for semantic text understanding and information
extraction. Popular methods separately address the Mention Detection (MD) and Entity …
extraction. Popular methods separately address the Mention Detection (MD) and Entity …
Refined: An efficient zero-shot-capable approach to end-to-end entity linking
We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained
entity types and entity descriptions to perform linking. The model performs mention …
entity types and entity descriptions to perform linking. The model performs mention …
EntQA: Entity linking as question answering
A conventional approach to entity linking is to first find mentions in a given document and
then infer their underlying entities in the knowledge base. A well-known limitation of this …
then infer their underlying entities in the knowledge base. A well-known limitation of this …
Global entity disambiguation with BERT
We propose a global entity disambiguation (ED) model based on BERT. To capture global
contextual information for ED, our model treats not only words but also entities as input …
contextual information for ED, our model treats not only words but also entities as input …
Joint learning of named entity recognition and entity linking
Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks,
since in order to perform EL, first the mentions to entities have to be detected. However, most …
since in order to perform EL, first the mentions to entities have to be detected. However, most …
ExtEnD: Extractive entity disambiguation
Abstract Local models for Entity Disambiguation (ED) have today become extremely
powerful, in most part thanks to the advent of large pre-trained language models. However …
powerful, in most part thanks to the advent of large pre-trained language models. However …