The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Exploiting Document Knowledge for Aspect-level Sentiment Classification
Open accessAbstract
Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification.However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models.In this paper, we explore two approaches that transfer knowledge from documentlevel data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification.We demonstrate the effectiveness of our approaches on 4 public datasets from Se-mEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Attention-based LSTM for Aspect-level Sentiment Classification
Wang, Y., Huang, M., zhu, X., & Zhao, L. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1058 · Cited by 2,450
Unsupervised Pretraining for Sequence to Sequence Learning
Ramachandran, P., Liu, P., & Le, Q. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1039 · Cited by 251
Cited by
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.005 · Cited by 745
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
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
Metadata
- Authors
- He, R., Lee, W. S., Ng, H. T., & Dahlmeier, D.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2018
- Pages
- 579-585
- OpenAlex
- W2964098749
- Cited by
- 215
- References
- 6
- FWCI
- 13.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- He, R., Lee, W. S., Ng, H. T., & Dahlmeier, D. (2018). Exploiting Document Knowledge for Aspect-level Sentiment Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). https://doi.org/10.18653/v1/p18-2092
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