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        <title>Robust Candidate Generation for Entity Linking on Short Social Media Texts</title>
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        <namePart type="given">Liam</namePart>
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    <name type="personal">
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    <abstract>Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.</abstract>
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%0 Conference Proceedings
%T Robust Candidate Generation for Entity Linking on Short Social Media Texts
%A Hebert, Liam
%A Makki, Raheleh
%A Mishra, Shubhanshu
%A Saghir, Hamidreza
%A Kamath, Anusha
%A Merhav, Yuval
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F hebert-etal-2022-robust
%X Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.
%U https://aclanthology.org/2022.wnut-1.8
%P 83-89

Markdown (Informal)

[Robust Candidate Generation for Entity Linking on Short Social Media Texts](https://aclanthology.org/2022.wnut-1.8) (Hebert et al., WNUT 2022)

• Robust Candidate Generation for Entity Linking on Short Social Media Texts (Hebert et al., WNUT 2022)

ACL

• Liam Hebert, Raheleh Makki, Shubhanshu Mishra, Hamidreza Saghir, Anusha Kamath, and Yuval Merhav. 2022. Robust Candidate Generation for Entity Linking on Short Social Media Texts. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 83–89, Gyeongju, Republic of Korea. Association for Computational Linguistics.