Cross-lingual Entity Linking (XEL) grounds mentions of entities that appear in a foreign (source) language text into an English (target) knowledge base (KB) such as Wikipedia . XEL consists of two steps: candidate generation, which retrieves a list of candidate entities for each mention, followed by candidate ranking. XEL methods have been successful on high-resource languages, but generally perform poorly on low-resource languages due to lack of supervision. In this paper , we show a thorough analysis on existing low-resource XEL methods, especially on their candidate generation methods and limitations. We observed several interesting findings: 1. They are heavily limited by the Wikipedia bilingual resource coverage. 2. They perform better on Wikipedia text than on real-world text such as news or twitter. In this paper, we claim that , under the low-resource language setting , outside-Wikipedia cross-lingual resources are essential. To prove this argument, we propose a simple but effective zero-shot framework, CogCompXEL, that complements current methods by utilizing query log mapping files from online search engines . CogCompXEL outperforms current state-of-the-art models on almost all 25 languages of the LORELEI dataset, achieving an absolute average increase of 25 \% in gold candidate recall.%DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > in gold candidate recall and of 13\\% in end-to-end linking accuracy over state-of-the-art baselines.
Cross-lingual Entity Linking (XEL) , the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia , has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia's interlanguage links and thus suffer when the foreign language's Wikipedia is small. We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages , QuEL~shows an average increase of 25 %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > in gold candidate recall and of 13\\% in end-to-end linking accuracy over state-of-the-art baselines.
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