Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields

arXiv:2404.09383v1 Announce Type: new
Abstract: 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 …

abstract annotation art arxiv cross-lingual cs.cl fields however languages low nlp paper performance random recognition state systems type world