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Gonçalves, S., Cortez, P. & Moro, S. (2018). A deep learning approach for sentence classification of scientific abstracts . In V. Kurkova et al. (Ed.), Artificial Neural Networks and Machine Learning – ICANN 2018. (pp. 479-488). Island of Rhodes, Greece: Springer.
S. Gonçalves et al., " A deep learning approach for sentence classification of scientific abstracts ", in Artificial Neural Networks and Machine Learning – ICANN 2018, V. Kurkova et al., Ed., Island of Rhodes, Greece, Springer, 2018, pp. 479-488
@inproceedings{gonçalves2018_1714242830931, author = "Gonçalves, S. and Cortez, P. and Moro, S.", title = " A deep learning approach for sentence classification of scientific abstracts ", booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2018", year = "2018", editor = "V. Kurkova et al.", volume = "", number = "", series = "", doi = "10.1007/978-3-030-01424-7_47", pages = "479-488", publisher = "Springer", address = " Island of Rhodes, Greece", organization = "European Neural Network Society", url = "https://link.springer.com/chapter/10.1007%2F978-3-030-01424-7_47" }
TY - CPAPER TI - A deep learning approach for sentence classification of scientific abstracts T2 - Artificial Neural Networks and Machine Learning – ICANN 2018 AU - Gonçalves, S. AU - Cortez, P. AU - Moro, S. PY - 2018 SP - 479-488 SN - 0302-9743 DO - 10.1007/978-3-030-01424-7_47 CY - Island of Rhodes, Greece UR - https://link.springer.com/chapter/10.1007%2F978-3-030-01424-7_47 AB - 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. ER -