Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts

Di Jin, Peter Szolovits


Abstract
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.
Anthology ID:
D18-1349
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3100–3109
Language:
URL:
https://aclanthology.org/D18-1349
DOI:
10.18653/v1/D18-1349
Bibkey:
Cite (ACL):
Di Jin and Peter Szolovits. 2018. Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3100–3109, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts (Jin & Szolovits, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1349.pdf
Video:
 https://vimeo.com/305946571
Code
 jind11/HSLN-Joint-Sentence-Classification
Data
PubMed RCTPubmed