arXiv:1705.06273cs.CL
Transfer Learning for Named-Entity Recognition with Neural Networks
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Abstract
Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.
Transfer learning is great, but is still pretty limited; the main success stories are retraining CNNs from ImageNet to other datasets and word2vec/GloVE. In the space of all the things you would want to do with ML, this is pretty limited. [EDIT]: I stumbled upon a transfer…
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