Papers with Cross-lingual entity linking

2 papers
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking (2020.tacl-1)

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Challenge: Existing approaches to cross-lingual entity linking (XEL) do not extend well to low-resource languages with few Wikipedia pages.
Approach: They propose to improve the model by combining Wikipedia references with a list of plausible candidate entities.
Outcome: The proposed method yields 16.9% in Top-30 gold candidate recall compared with state-of-the-art models.
Towards Zero-resource Cross-lingual Entity Linking (D19-61)

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Challenge: XEL is challenging for most languages because of limited availability of requisite resources . simulated environments that use significant resources are not available in truly low-resource languages .
Approach: They propose improvements to entity candidate generation and disambiguation to make better use of the limited resources available in low-resource languages.
Outcome: The proposed model gains 6-20% end-to-end linking accuracy on four low-resource languages.

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