Understand
Natural Language Processing (NLP) and especially natural language text analysis have seen great advances in recent times.
- Usage of deep learning in text processing has revolutionized the techniques for text processing and achieved remarkable results.
- Different deep learning architectures like CNN, LSTM, and very recent Transformer have been used to achieve state of the art results variety on NLP tasks.
- In this work, we survey a host of deep learning architectures for text classification tasks.
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Arora, P.: Sentiment analysis for hindi language (2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
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)
2014
Earlier work this paper cites.
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI conference on artificial intelligence (2015)
2015
Earlier work this paper cites.
Similar
2016
Cited alongside, same era.
Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., Xu, B.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 207–212 (2016)
2016
Cited alongside, same era.
2018
Cited alongside, same era.
Then
2018
Later among the works it cites.
2018
Later among the works it cites.
Tummalapalli, M., Chinnakotla, M., Mamidi, R.: Towards better sentence classification for morphologically rich languages (2018)
2018
Later among the works it cites.
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