The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Transformation Networks for Target-Oriented Sentiment Classification
Open accessAbstract
Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence.RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance.After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues.Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer.Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer.Experiments show that our model achieves a new state-of-the-art performance on a few benchmarks. 1
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
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Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Representation Learning: A Review and New Perspectives
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Identity mappings in deep residual networks
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Identity Mappings in Deep Residual Networks
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Effective Approaches to Attention-based Neural Machine Translation
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Cited by
A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
Li, X., Bing, L., Zhang, W., & Lam, W. (2019) · Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) · doi:10.18653/v1/d19-5505 · Cited by 348
Self-citationTarget-level sentiment analysis for news articles
Žitnik, S., Blagus, N., & Bajec, M. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108939 · Cited by 32
Metadata
- Authors
- Li, X., Bing, L., Lam, W., & Shi, B.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 946-956
- OpenAlex
- W2963240575
- Cited by
- 513
- References
- 38
- FWCI
- 27.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Li, X., Bing, L., Lam, W., & Shi, B. (2018). Transformation Networks for Target-Oriented Sentiment Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p18-1087
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