Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

Improving Candidate Generation for Low-resource Cross-lingual Entity Linking Shuyan Zhou, Shruti Rijhwani, John Wieting Jaime Carbonell, Graham Neubig Language Technologies Institute Carnegie Mellon University {shuyanzh,srijhwan,jwieting,jgc,gneubig}@cs.cmu.edu Abstract Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Ap- proaches based on resources from Wikipedia have proven successful in the realm of rela- tively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low- resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource coun- terparts. In this paper, we first assess the prob- lemsfacedbycurrententitycandidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the dis- connect between entity mentions and KB en- tries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in TOP-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.1 1 Introduction Entity linking (EL; Bunescu and Pas¸ca, 2006; Cucerzan, 2007; Dredze et al., 2010; Hoffart 1Code and data will be released. et al., 2011) associates entity mentions in a document with their entries in a knowledge base (KB). In this work, we focus on cross-lingual entity linking (XEL; McNamee et al., 2011; Ji et al., 2015) where the documents are in a source language that differs from the KB language (target). XEL is an important component task for information extraction in languages that do not have extensive KB resources, and can potentially benefit downstream applications such as cross- lingual building question answering systems (Veyseh, 2016), or supporting international hu- manitarian assistance efforts in areas that do not speak English (Strassel et al., 2017; Min et al., 2019). Following Sil et al. (2018) and Upadhyay et al. (2018a), we consider the target language KB to be English Wikipedia. Given a document and named entity mentions identified by a Named Entity Recognition (NER) model, there are two primary steps in an XEL system: (1) candidate generation, in which a model retrieves a short list of plausible KB entities for each mention and (2) disambiguation, in which a model selects the most likely KB entity from the candidate list. The quality of candidate lists will influence the performance of the end-to-end XEL system, as correct entities not included in this list will not be recovered by the disambiguation model. InmonolingualEL, candidategeneration hasoften been considered trivial (Shen et al., 2015). Simple approaches using string similarity or Wikipedia anchor-text links produce mention-entity lookup tables with high candidate recalls (e.g., in the 90% …

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