Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

Franck Dernoncourt, Ji Young Lee, Peter Szolovits


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-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.
Anthology ID:
E17-2110
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
694–700
Language:
URL:
https://aclanthology.org/E17-2110
DOI:
Bibkey:
Cite (ACL):
Franck Dernoncourt, Ji Young Lee, and Peter Szolovits. 2017. Neural Networks for Joint Sentence Classification in Medical Paper Abstracts. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 694–700, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts (Dernoncourt et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-2110.pdf
Code
 additional community code