MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

TL;DR

MAG introduces a multilingual, knowledge-base agnostic, and deterministic entity linking method that outperforms traditional models, especially in non-English languages, by combining context retrieval and graph algorithms.

Contribution

The paper presents MAG, a novel approach that eliminates the need for language-specific training data in entity linking, leveraging knowledge bases and graph algorithms.

Findings

MAG achieves state-of-the-art results on English datasets.

MAG outperforms English-trained models on non-English datasets.

The approach is effective across 23 datasets and 7 languages.

Abstract

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.