TITLE: XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment 

ABSTRACT: Cross-lingual named-entity lexica are an important resource to multilingual
NLP tasks such as machine translation and cross-lingual wikification. While
knowledge bases contain a large number of entities in high-resource languages
such as English and French, corresponding entities for lower-resource languages
are often missing. To address this, we propose Lexical-Semantic-Phonetic Align
(LSP-Align), a technique to automatically mine cross-lingual entity lexica from
mined web data. We demonstrate LSP-Align outperforms baselines at extracting
cross-lingual entity pairs and mine 164 million entity pairs from 120 different
languages aligned with English. We release these cross-lingual entity pairs
along with the massively multilingual tagged named entity corpus as a resource
to the NLP community.
Cross-lingual named-entity lexica are an important resource to multilingual
NLP tasks such as machine translation and cross-lingual wikification. While
knowledge bases contain a large number of entities in high-resource languages
such as English and French, corresponding entities for lower-resource languages
are often missing. To address this, we propose Lexical-Semantic-Phonetic Align
(LSP-Align), a technique to automatically mine cross-lingual entity lexica from
mined web data. We demonstrate LSP-Align outperforms baselines at extracting
cross-lingual entity pairs and mine 164 million entity pairs from 120 different
languages aligned with English. We release these cross-lingual entity pairs
along with the massively multilingual tagged named entity corpus as a resource
to the NLP community.
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
http://arxiv.org/abs/2104.08597v2