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Zero-Resource Cross-Lingual Named Entity Recognition
[article]
2019
arXiv
pre-print
Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or
arXiv:1911.09812v1
fatcat:fhj3krxbsre7jkzwocu327o2i4