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

Ahmed El-Kishky, Adithya Renduchintala, James Cross, Francisco Guzmán, Philipp Koehn


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.
Anthology ID:
2021.emnlp-main.814
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10424–10430
Language:
URL:
https://aclanthology.org/2021.emnlp-main.814
DOI:
10.18653/v1/2021.emnlp-main.814
Bibkey:
Cite (ACL):
Ahmed El-Kishky, Adithya Renduchintala, James Cross, Francisco Guzmán, and Philipp Koehn. 2021. XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10424–10430, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment (El-Kishky et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.814.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.814.mp4
Data
XLEnt