Robust Candidate Generation for Entity Linking on Short Social Media Texts

• URL: http://arxiv.org/abs/2210.07472v1
• Date: Fri, 14 Oct 2022 02:47:31 GMT
• Title: Robust Candidate Generation for Entity Linking on Short Social Media Texts
• Authors: Liam Hebert and Raheleh Makki and Shubhanshu Mishra and Hamidreza Saghir and Anusha Kamath and Yuval Merhav
• Abstract summary: We show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity. We demonstrate a hybrid solution using long contextual representation from Wikipedia, achieving 0.93 recall.
• Score: 1.5006258585503875
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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