text: 137,21,1166334749729857536,185910194,Graham Neubig,"#EMNLP2019 paper ""A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource NER"", examines cross-lingual transfer and active learning to create NER systems in new languages: <LINK> <LINK> Interestingly, even a little annotation is enough to get quite good results, only a few 100 or 1000 entities. Differences between simulation and human annotation are also interesting.",https://arxiv.org/abs/1908.08983,"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. ","A Little Annotation does a Lot of Good: A Study in Bootstrapping