This is not the latest version of this item. The latest version can be found here.
Hierarchical neural networks for sequential sentence classification in medical scientific abstracts
Name
1808.06161.pdf
Description
Accepted version
Size
470.19 KB
Format
Adobe PDF
Checksum (MD5)
250908f909dd12d603fb61d4bf9c570f
Author(s)
Jin, D
Szolovits, P
Date Issued
October 2018
Journal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Citation
Jin, D and Szolovits, P. 2018. "Hierarchical neural networks for sequential sentence classification in medical scientific abstracts." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018.
Version
Author's final manuscript
Abstract
© 2018 Association for Computational Linguistics Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.aclweb.org/anthology/D18-1349/
