|
[1] G. Arevian. Recurrent neural networks for robust real-world text classification. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pages 326–329. IEEE Computer Society, 2007. [2] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016. [3] L. Chen, C. Zhang, and C. Wilson. Tweeting under pressure: Analyzing trending topics and evolving word choice on sina weibo. In Proceedings of the First ACM Conference on Online Social Networks, COSN ’13, pages 89–100, New York, NY,USA, 2013. ACM. [4] K. Dashtipour, S. Poria, A. Hussain, E. Cambria, A. Y. A. Hawalah, A. Gelbukh, and Q. Zhou. Multilingual sentiment analysis: State of the art and independent comparison of techniques. Cognitive Computation, 8(4):757–771, Aug 2016. [5] K.-w. Fu and M. Chau. Reality check for the chinese microblog space: a random sampling approach. PloS one, 8(3):e58356, 2013. [6] T. Ge, K. He, Q. Ke, and J. Sun. Optimized product quantization for approximate nearest neighbor search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2946–2953, 2013. [7] H. J´egou, R. Tavenard, M. Douze, and L. Amsaleg. Searching in one billion vectors: re-rank with source coding. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pages 861–864. IEEE, 2011. [8] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. J´egou, and T. Mikolov. Fasttext.zip: Compressing text classification models. arXiv preprint arXiv:1612.03651,2016. [9] T. Kenter, A. Borisov, and M. de Rijke. Siamese cbow: Optimizing word embeddings for sentence representations. 2016.27 [10] Y. Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014. [11] R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, A. Torralba, R. Urtasun, and S. Fidler. Skip-thought vectors. arXiv preprint arXiv:1506.06726, 2015. [12] Q. V. Le and T. Mikolov. Distributed representations of sentences and documents icml. 2014. [13] T. Mikolov, Q. V. Le, and I. Sutskever. Exploiting similarities among languages for machine translation. CoRR, abs/1309.4168, 2013. [14] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. pages 3111–3119, 2013. [15] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 79–86. Association for Computational Linguistics, 2002. [16] D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin. Learning sentiment-specific word embedding for twitter sentiment classification. In ACL (1), pages 1555–1565,2014. [17] D. Vilares, M. Alonso Pardo, and C. G´omez-Rodr´ıguez. Supervised sentiment analysis in multilingual environments. 53, 05 2017. [18] J. Zhao, L. Dong, J. Wu, and K. Xu. Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1528–1531. ACM, 2012.
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