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
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1349.pdf
- Code
- jind11/HSLN-Joint-Sentence-Classification
- Data
- PubMed RCT, Pubmed