Global Entity Ranking Across Multiple Languages

Abstract

We present work on building a global long-tailed ranking of entities across multiple languages using Wikipedia and Freebase knowledge bases. We identify multiple features and build a model to rank entities using a ground-truth dataset of more than 10 thousand labels. The final system ranks 27 million entities with 75% precision and 48% F1 score. We provide performance evaluation and empirical evidence of the quality of ranking across languages, and open the final ranked lists for future research.

2 Pages, 1 Figure, 2 Tables, WWW2017 Companion, WWW 2017 Companion

References in corpus (1)

• High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data

Cited by in corpus (3)

• DAWT: Densely Annotated Wikipedia Texts across multiple languages
• High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data
• Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media