Computational study demonstrates scalable name tagging and linking across 282 languages, highlighting effective knowledge base transfer without manual annotation.
The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating "silver-standard" annotations by transferring annotations from English to other languages through crosslingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from crosslingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data. All the data sets, resources and systems for 282 languages are made publicly available as a new benchmark 1 .
No takes yet. Share an insight, caveat, or question.
Pan et al. (2017) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: