Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/16641
Author(s): | Gonçalves, S. Cortez, P. Moro, S. |
Editor: | V. Kurkova et al. |
Date: | 2018 |
Title: | A deep learning approach for sentence classification of scientific abstracts |
Pages: | 479 - 488 |
ISSN: | 0302-9743 |
DOI (Digital Object Identifier): | 10.1007/978-3-030-01424-7_47 |
Keywords: | Bi-directional gated recurrent unit Sentence classification Text mining Deep learning Scientific articles |
Abstract: | The classification of abstract sentences is a valuable tool to support scientific database querying, to summarize relevant literature works and to assist in the writing of new abstracts. This study proposes a novel deep learning approach based on a convolutional layer and a bi-directional gated recurrent unit to classify sentences of abstracts. The proposed neural network was tested on a sample of 20 thousand abstracts from the biomedical domain. Competitive results were achieved, with weight-averaged precision, recall and F1-score values around 91%, which are higher when compared to a state-of-the-art neural network. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
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2018_ICANN-GoncalvesCortezMoro-PosPrint.pdf | Pós-print | 400,47 kB | Adobe PDF | View/Open |
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