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.
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References
Atanassova, I., Bertin, M., Larivière, V.: On the composition of scientific abstracts. J. Doc. 72(4), 636–647 (2016)
Boudin, F., Nie, J.Y., Bartlett, J.C., Grad, R., Pluye, P., Dawes, M.: Combining classifiers for robust pico element detection. BMC Med. Inform. Decis. Mak. 10(1), 29 (2010)
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734. Association for Computational Linguistics, Doha, Qatar, October 2014
Cornuel, E.: A vision for Business Schools, vol. 24. Emerald Group Publishing (2005)
Dernoncourt, F., Lee, J.Y.: Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing, vol. 2, pp. 308–313 (2017)
Dernoncourt, F., Lee, J.Y., Szolovits, P.: Neural networks for joint sentence classification in medical paper abstracts. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 2, pp. 694–700 (2017)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)
Khabsa, M., Giles, C.L.: The number of scholarly documents on the public web. PloS One 9(5), e93949 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Kitchenham, B., Brereton, P.: A systematic review of systematic review process research in software engineering. Inf. Softw. Technol. 55(12), 2049–2075 (2013)
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256 (2010)
Liu, Y., Wu, F., Liu, M., Liu, B.: Abstract sentence classification for scientific papers based on transductive SVM. Comput. Inf. Sci. 6(4), 125 (2013)
Michalska-Smith, M.J., Allesina, S.: And, not or: quality, quantity in scientific publishing. PloS One 12(6), e0178074 (2017)
Moro, S., Cortez, P., Rita, P.: Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent dirichlet allocation. Expert. Syst. Appl. 42(3), 1314–1324 (2015)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532–1543 (2014)
Witten, I., Frank, E., Hall, M., Pal, C.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, San Franscico (2017)
Acknowledgements
This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
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Gonçalves, S., Cortez, P., Moro, S. (2018). A Deep Learning Approach for Sentence Classification of Scientific Abstracts. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_47
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