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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Aditi Chaudhary
- sl:arxiv_num : 1908.08983
- sl:arxiv_published : 2019-08-23T19:15:07Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers@en
- sl:arxiv_updated : 2019-08-23T19:15:07Z
- sl:bookmarkOf : https://arxiv.org/abs/1908.08983
- sl:creationDate : 2019-08-28
- sl:creationTime : 2019-08-28T22:57:43Z
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