Entity linking (EL) is the task of disambiguating mentions in text by
associating them with entries in a predefined database of mentions (persons,
organizations, etc). Most previous EL research has focused mainly on one
language, English, with less attention being paid to other languages, such as
Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent
Entity Linking system, which provides an EL framework which, once trained on
one language, works remarkably well on a number of different languages without
change. LIEL makes a joint global prediction over the entire document,
employing a discriminative reranking framework with many domain and
language-independent feature functions. Experiments on numerous benchmark
datasets, show that the proposed system, once trained on one language, English,
outperforms several state-of-the-art systems in English (by 4 points) and the
trained model also works very well on Spanish (14 points better than a
competitor system), demonstrating the viability of the approach.

本文介绍了 LIEL，一种语言无关的实体链接系统，通过使用区分重排框架和许多不受领域和语言限制的特征函数，它可以在多种不同的语言中工作。在各种基准数据集上的实验表明，该系统在英语上表现出色，在训练过英语的情况下，也可以很好地工作在西班牙语上，这证明了该方法的可行性。