Entity Disambiguation
58 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
Deep Joint Entity Disambiguation with Local Neural Attention
We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
amazon-research/ReFinED
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NAACL (ACL) 2022
The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
Improving Entity Disambiguation by Reasoning over a Knowledge Base
amazon-science/ReFinED
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NAACL 2022
Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types.
EntEval: A Holistic Evaluation Benchmark for Entity Representations
ZeweiChu/EntEval
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IJCNLP 2019
Rich entity representations are useful for a wide class of problems involving entities.
Learning Dynamic Context Augmentation for Global Entity Linking
YoungXiyuan/DCA
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IJCNLP 2019
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space.
Autoregressive Entity Retrieval
facebookresearch/GENRE
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ICLR 2021
For instance, Encyclopedias such as Wikipedia are structured by entities (e. g., one per Wikipedia article).
Biomedical Interpretable Entity Representations
diegoolano/biomedical_interpretable_entity_representations
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Findings (ACL) 2021
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.
Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
epfl-dlab/gcd
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In this work, we demonstrate that formal grammars can describe the output space for a much wider range of tasks and argue that GCD can serve as a unified framework for structured NLP tasks in general.
Entity Disambiguation with Web Links
Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories.
Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood.
