One for All: Towards Language Independent Named Entity Linking
This addresses the limitation of most entity linking research focusing on English, offering a solution for multilingual applications.
The paper tackles the problem of entity linking across multiple languages by introducing LIEL, a language-independent system that, once trained on English, outperforms state-of-the-art systems in English by 4 points and in Spanish by 14 points.
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.