Transfer learning for named-entity recognition with neural networks
Name
1705.06273.pdf
Description
Submitted version
Size
272.84 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Lee, Ji Young
Dernoncourt, Franck
Szolovits, Peter
Date Issued
May 2018
Journal
LREC 2018: Eleventh International Conference on Language Resources and Evaluation
Publisher
European Language Resources Association
Citation
Lee, Ji Young et al. "Transfer Learning for Named-Entity Recognition with Neural Networks." LREC 2018: Eleventh International Conference on Language Resources and Evaluation, May 2018, Miyazaki, Japan, European Language Resources Association, May 2018 © 2018 LREC
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://www.lrec-conf.org/proceedings/lrec2018/index.html
