Papers › EntQA: Entity Linking as Question Answering

EntQA: Entity Linking as Question Answering

5 Oct 2021ICLR 2022 4arXiv:2110.02369archive 2025-07-28

Wenzheng Zhang, Wenyue Hua, Karl Stratos

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 approach is that it requires finding mentions without knowing their entities, which is unnatural and difficult. We present a new model that does not suffer from this limitation called EntQA, which stands for Entity linking as Question Answering. EntQA first proposes candidate entities with a fast retrieval module, and then scrutinizes the document to find mentions of each candidate with a powerful reader module. Our approach combines progress in entity linking with that in open-domain question answering and capitalizes on pretrained models for dense entity retrieval and reading comprehension. Unlike in previous works, we do not rely on a mention-candidates dictionary or large-scale weak supervision. EntQA achieves strong results on the GERBIL benchmarking platform.

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wenzhengzhang/entqa officialmentioned in papermentioned on GitHubpytorch report
epfl-dlab/multilingual-entity-insertion mentioned on GitHubpytorch report

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MultiLabelLoss wenzhengzhang/entqa/reader.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · b90ec9be4e228a9b · report
Reader wenzhengzhang/entqa/reader.py official repository ran · metamorphic tier: deterministic MIT (permissive) · 28b3ff1baa3a4fc0 · report
evaluate_rerank WenzhengZhang/EntQA/run_reader.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 1e1ea0c91e821881 · report
log_sum_loss wenzhengzhang/entqa/reader.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 8a6d167f6d5f9ff0 · report
max_min_loss wenzhengzhang/entqa/reader.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · ea3178f6466d95f6 · report
sum_log_loss wenzhengzhang/entqa/reader.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · bf985e51c9b5c737 · report
sum_log_nce_loss wenzhengzhang/entqa/reader.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 845b2678876fab1d · report
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Tasks

BenchmarkingEntity LinkingEntity RetrievalOpen-Domain Question AnsweringQuestion AnsweringReading ComprehensionRetrieval

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Entity Linking AIDA-CoNLL Zhang et al. (2021) Micro-F1 strong 85.8 #5 of 17 Archive leaderboard report

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