Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
Name
1612.05251.pdf
Description
Submitted version
Size
325.5 KB
Format
Adobe PDF
Checksum (MD5)
9db7ecdc4062c907fa8288aad94e0442
Author(s)
Dernoncourt, Franck
Lee, Ji Young
Szolovits, Peter
Date Issued
April 2017
Journal
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Publisher
Association for Computational Linguistics
Citation
Dernoncourt, Franck, et al. “Neural Networks for Joint Sentence Classification in Medical Paper Abstracts.” Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, April, 2019, Valencia, Spain, Association for Computational Linguistics, 2017: 694–700.
Version
Original manuscript
Abstract
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model outperforms the state-ofthe- art results on two different datasets for sequential sentence classification in medical abstracts.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/v1/e17-2110
