Entity Disambiguation
56 papers with code • 11 benchmarks • 12 datasets
Entity Disambiguation is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.
Source: Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
Benchmarks
These leaderboards are used to track progress in Entity Disambiguation
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Datasets
CoNLL
AIDA CoNLL-YAGO
AQUAINT
TAC 2010
Wikidata-Disamb
Most implemented papers
End-to-End Neural Entity Linking
Entity Linking (EL) is an essential task for semantic text understanding and information extraction.
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation
anderbarrena/500kNED
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CONLL 2018
Named Entity Disambiguation algorithms typically learn a single model for all target entities.
Named Entity Disambiguation using Deep Learning on Graphs
We tackle \ac{NED} by comparing entities in short sentences with \wikidata{} graphs.
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.
Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts
kingsaint/Wikidata-Descriptions
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Despite being vast repositories of factual information, cross-domain knowledge graphs, such as Wikidata and the Google Knowledge Graph, only sparsely provide short synoptic descriptions for entities.
Boosting Entity Linking Performance by Leveraging Unlabeled Documents
lephong/wnel
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ACL 2019
First, we construct a high recall list of candidate entities for each mention in an unlabeled document.
Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning
However, most neural collective EL methods depend entirely upon neural networks to automatically model the semantic dependencies between different EL decisions, which lack of the guidance from external knowledge.
Uncovering the Semantics of Wikipedia Categories
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like YAGO or Probase, and has been used extensively for tasks like entity disambiguation or semantic similarity estimation.
Global Entity Disambiguation with BERT
studio-ousia/luke
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NAACL 2022
We propose a global entity disambiguation (ED) model based on BERT.
Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking
samuelbroscheit/entity_knowledge_in_bert
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CONLL 2019
We show on an entity linking benchmark that (i) this model improves the entity representations over plain BERT, (ii) that it outperforms entity linking architectures that optimize the tasks separately and (iii) that it only comes second to the current state-of-the-art that does mention detection and entity disambiguation jointly.
