EntQA: Entity Linking as Question Answering

ICLR 2022  ·  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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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Entity Linking AIDA-CoNLL Zhang et al. (2021) Micro-F1 strong 85.8 # 1

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