@inproceedings{zhou-etal-2019-towards,
title = "Towards Zero-resource Cross-lingual Entity Linking",
author = "Zhou, Shuyan and
Rijhwani, Shruti and
Neubig, Graham",
editor = "Cherry, Colin and
Durrett, Greg and
Foster, George and
Haffari, Reza and
Khadivi, Shahram and
Peng, Nanyun and
Ren, Xiang and
Swayamdipta, Swabha",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6127",
doi = "10.18653/v1/D19-6127",
pages = "243--252",
abstract = "Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, many works on XEL have been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are not available in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. We next propose three improvements to both entity candidate generation and disambiguation that make better use of the limited resources we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-20{\%} end-to-end linking accuracy.",
}
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<abstract>Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, many works on XEL have been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are not available in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. We next propose three improvements to both entity candidate generation and disambiguation that make better use of the limited resources we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-20% end-to-end linking accuracy.</abstract>
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%0 Conference Proceedings
%T Towards Zero-resource Cross-lingual Entity Linking
%A Zhou, Shuyan
%A Rijhwani, Shruti
%A Neubig, Graham
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhou-etal-2019-towards
%X Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, many works on XEL have been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are not available in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. We next propose three improvements to both entity candidate generation and disambiguation that make better use of the limited resources we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-20% end-to-end linking accuracy.
%R 10.18653/v1/D19-6127
%U https://aclanthology.org/D19-6127
%U https://doi.org/10.18653/v1/D19-6127
%P 243-252
Markdown (Informal)
[Towards Zero-resource Cross-lingual Entity Linking](https://aclanthology.org/D19-6127) (Zhou et al., 2019)
ACL
- Shuyan Zhou, Shruti Rijhwani, and Graham Neubig. 2019. Towards Zero-resource Cross-lingual Entity Linking. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 243–252, Hong Kong, China. Association for Computational Linguistics.