LIEL is a Language Independent Entity Linking system. It works remarkably well on a number of different languages without change. LIEL makes a joint global prediction over the entire document.
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.