Neural Cross-Lingual Entity Linking

Authors

  • Avirup Sil IBM Research AI
  • Gourab Kundu IBM
  • Radu Florian IBM
  • Wael Hamza IBM

Keywords:

NLP, Information Extraction, Entity Linking, Entity Disambiguation

Abstract

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.

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Published

2018-04-27

How to Cite

Sil, A., Kundu, G., Florian, R., & Hamza, W. (2018). Neural Cross-Lingual Entity Linking. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11964