ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages using Wikidata

TL;DR

ParaNames 1.0 is a comprehensive multilingual entity name corpus derived from Wikidata, covering over 400 languages and 16.8 million entities, enhancing multilingual NLP tasks like translation and NER.

Contribution

This paper introduces ParaNames, the largest multilingual name resource from Wikidata, with a standardized hierarchy and demonstrated utility in translation and named entity recognition tasks.

Findings

Improved translation accuracy across 17 languages.

Enhanced NER performance on 10 languages.

Largest resource of its kind to date.

Abstract

We introduce ParaNames, a massively multilingual parallel name resource consisting of 140 million names spanning over 400 languages. Names are provided for 16.8 million entities, and each entity is mapped from a complex type hierarchy to a standard type (PER/LOC/ORG). Using Wikidata as a source, we create the largest resource of this type to date. We describe our approach to filtering and standardizing the data to provide the best quality possible. ParaNames is useful for multilingual language processing, both in defining tasks for name translation/transliteration and as supplementary data for tasks such as named entity recognition and linking. We demonstrate the usefulness of ParaNames on two tasks. First, we perform canonical name translation between English and 17 other languages. Second, we use it as a gazetteer for multilingual named entity recognition, obtaining performance improvements on all 10 languages evaluated.