Most state-of-the-art models for named entity recognition (NER) rely on the
availability of large amounts of labeled data, making them challenging to
extend to new, lower-resourced languages. However, there are now several
proposed approaches involving either cross-lingual transfer learning, which
learns from other highly resourced languages, or active learning, which
efficiently selects effective training data based on model predictions. This
paper poses the question: given this recent progress, and limited human
annotation, what is the most effective method for efficiently creating
high-quality entity recognizers in under-resourced languages? Based on
extensive experimentation using both simulated and real human annotation, we
find a dual-strategy approach best, starting with a cross-lingual transferred
model, then performing targeted annotation of only uncertain entity spans in
the target language, minimizing annotator effort. Results demonstrate that
cross-lingual transfer is a powerful tool when very little data can be
annotated, but an entity-targeted annotation strategy can achieve competitive
accuracy quickly, with just one-tenth of training data.

该研究探讨了在资源匮乏的语言中高效创建高质量实体识别器的最有效方法，通过运用跨语言转移、有针对性注释同属于两种策略的方法，实现在小数据集下快速精确识别实体的结果。