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
Latest papers
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
wengsyx/lingyi
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The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities.
Highly Parallel Autoregressive Entity Linking with Discriminative Correction
nicola-decao/efficient-autoregressive-EL
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EMNLP 2021
Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i. e., joint mention detection and disambiguation).
SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles
To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking.
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases
Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates.
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.
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.
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.
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.
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.
