Entity Disambiguation
57 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
A Recurrent Model for Collective Entity Linking with Adaptive Features
Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models.
Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets
To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance.
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
mulangonando/Impact-of-KG-Context-on-ED
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We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
debayan/pnel
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In such a pipeline, Entity Linking (EL) is often the first step.
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities.
Entity Linking in 100 Languages
hazyresearch/tabi
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EMNLP 2020
We propose a new formulation for multilingual entity linking, where language-specific mentions resolve to a language-agnostic Knowledge Base.
Fast and Effective Biomedical Entity Linking Using a Dual Encoder
kingsaint/BioMedical-EL
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EACL (Louhi) 2021
Additionally, we modify our dual encoder model for end-to-end biomedical entity linking that performs both mention span detection and entity disambiguation and out-performs two recently proposed models.
Multilingual Autoregressive Entity Linking
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP
These experiments on AmbER sets show their utility as an evaluation tool and highlight the weaknesses of popular retrieval systems.
Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference
rloganiv/streaming-cdc
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ACL 2021
We investigate: how to best encode mentions, which clustering algorithms are most effective for grouping mentions, how models transfer to different domains, and how bounding the number of mentions tracked during inference impacts performance.
