Abstract
This work deals with curriculum learning for deep learning models for the sentiment analysis task. We design a new way of curriculum learning for text data. We reorder the training dataset to introduce the simpler examples first. We estimate the difficulty of the examples by measuring the length of the sentences. The simple examples are supposed to be shorter. We also experiment with measuring the frequency of the words, which is a technique designed by earlier researchers. We attempt to evaluate changes in the overall accuracy of the models using both curriculum learning techniques. Our experiments do not show an increase in accuracy for any of the methods. Nevertheless, we reach a new state of the art in the sentiment analysis for Czech as a by-product of our effort.
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References
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48. ACM (2009)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017). http://aclweb.org/anthology/Q17-1010
Cirik, V., Hovy, E., Morency, L.P.: Visualizing and understanding curriculum learning for long short-term memory networks. arXiv preprint arXiv:1611.06204 (2016)
Habernal, I., Ptácek, T., Steinberger, J.: Sentiment analysis in Czech social media using supervised machine learning. In: Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 65–74 (2013)
Hochreiter, S., Schmidhuber, J.: LSTM can solve hard long time lag problems. In: Advances in Neural Information Processing Systems, pp. 473–479 (1997)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Acknowledgement
This work has been supported by Grant No. SGS-2019-018 Processing of heterogeneous data and its specialized applications, and was partly supported from ERDF “Research and Development of Intelligent Components of Advanced Technologies for the Pilsen Metropolitan Area (InteCom)” and by the project LO1506 of the Czech Ministry of Education, Youth and Sports.
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Sido, J., Konopík, M. (2019). Curriculum Learning in Sentiment Analysis. In: Salah, A., Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science(), vol 11658. Springer, Cham. https://doi.org/10.1007/978-3-030-26061-3_45
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DOI: https://doi.org/10.1007/978-3-030-26061-3_45
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