MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
This addresses the challenge of porting entity linking systems to new languages, which is crucial for multilingual NLP applications, and represents a novel advancement rather than an incremental improvement.
The paper tackles the problem of entity linking across multiple languages without requiring language-specific training data, presenting MAG, a multilingual and knowledge-base agnostic approach that achieves state-of-the-art performance on English datasets and outperforms the best English-trained method by up to 0.6 in micro F-measure on non-English languages.
Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-based agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG, on the other hand, achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.