Low-Resource Named Entity Recognition with Cross-Lingual,
  Character-Level Neural Conditional Random Fields
Ryan Cotterell Kevin Duh
Low-resource named entity recognition is still an open problem in NLP. Most
state-of-the-art systems require tens of thousands of annotated sentences in
order to obtain high performance. However, for most of the world's languages,
it is unfeasible to obtain such annotation. In this paper, we present a
transfer learning scheme, whereby we train character-level neural CRFs to
predict named entities for both high-resource languages and low resource
languages jointly. Learning character representations for multiple related
languages allows transfer among the languages, improving F1 by up to 9.8 points
over a loglinear CRF baseline.