Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning for Named-Entity Recognition with Neural Networks   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.

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The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th