XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
This addresses the need for cross-lingual named-entity resources in multilingual NLP tasks like machine translation, though it is incremental as it builds on existing mining and alignment methods.
The paper tackled the problem of missing cross-lingual entity lexica for lower-resource languages by proposing LSP-Align, a technique to automatically mine such lexica from web data, resulting in 164 million entity pairs extracted from 120 languages aligned with English.
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.