Fine-grained Sentiment Classification using BERT

A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, June 2011, pp. 142–150

2011

Earlier work this paper cites.

R. Socher, J. Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning, “Semi-supervised recursive autoencoders for predicting sentiment distributions,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2011, pp. 151–161

2011

Earlier work this paper cites.

M. Schuster and K. Nakajima, “Japanese and korean voice search,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2012, pp. 5149–5152

2012

Earlier work this paper cites.

T. Mikolov, K. Chen, G. S. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” CoRR , vol. abs/1301.3781, 2013

Original

2013

Earlier work this paper cites.

R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2013, pp. 1631–1642

2013

Earlier work this paper cites.

Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in International Conference on Machine Learning , 2014, pp. 1188–1196

2014

Cited alongside, same era.

J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 1532–1543

2014

Cited alongside, same era.

Y. Kim, “Convolutional neural networks for sentence classification,” arXiv preprint arXiv:1408.5882 , 2014

Original

2014

Cited alongside, same era.

D. Chen and C. Manning, “A fast and accurate dependency parser using neural networks,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 740–750

2014

Cited alongside, same era.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014

K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree-structured long short-term memory networks,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2015, pp. 1556–1566

2015

Later among the works it cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017, pp. 5998–6008

2017

Later among the works it cites.

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Transactions of the Association for Computational Linguistics , vol. 5, pp. 135–146, 2017

2017

Later among the works it cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT , 2018

2018