Cross-lingual wikification using multilingual embeddings

Abstract

Cross-lingual Wikification is the task of grounding mentions written in non-English documents to entries in the English Wikipedia. This task involves the problem of comparing textual clues across languages, which requires developing a notion of similarity between text snippets across languages. In this paper, we address this problem by jointly training multilingual embeddings for words and Wikipedia titles. The proposed method can be applied to all languages represented in Wikipedia, including those for which no machine translation technology is available. We create a challenging dataset in 12 languages and show that our proposed approach outperforms various baselines. Moreover, our model compares favorably with the best systems on the TAC KBP2015 Entity Linking task including those that relied on the availability of translation from the target language to English.