Abstract
We present a comparative evaluation of two neural network architectures, which can be used to compute representations of phrases or sentences. The Semi-Supervised Recursive Autoencoder (SRAE) and the Convolutional Neural Network (CNN) are both methods that directly operate on sequences of words represented via word embeddings and jointly model the syntactic and semantic peculiarities of phrases. We compare both models with respect to their classification accuracy on the task of binary sentiment polarity classification. Our evaluation shows that a single-layer CNN produces equally accurate phrase representations and that both methods profit from the initialization with word embeddings trained by a language model. We observe that the initialization with domain specific word embeddings has no significant effect on the accuracy of both phrase models. A pruning experiment revealed that up to 95 % of the parameters used to train the CNN could be removed afterwards without affecting the model’s accuracy.
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References
Socher, R., Manning, C.D., Ng, A.Y.: Learning continuous phrase representations and syntactic parsing with recursive neural networks. In: Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop, pp. 1–9 (2010)
Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoder for predicting sentiment distributions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 151–161 (2011)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, p. 1642 (2013b)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167 (2008)
dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING) (2014)
Pollack, J.B.: Recursive distributed representations. Artif. Intell. 46(1), 77–105 (1990)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association Computational Linguistics, pp. 115–124 (2005)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Proc. Syst. 26, 3111–3119 (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
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Jurgovsky, J., Granitzer, M. (2015). Comparing Recursive Autoencoder and Convolutional Network for Phrase-Level Sentiment Polarity Classification. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_14
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DOI: https://doi.org/10.1007/978-3-319-19581-0_14
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