A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
| Approach: | They propose a method for bootstrapping named entity recognition models in under-resourced languages . they use cross-lingual transfer learning and targeted annotation of only uncertain entities . |
| Outcome: | The proposed method achieves competitive accuracy with just one-tenth of training data. |
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