The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
Open accessAbstract
In this paper, we investigate the modeling power of contextualized embeddings from pretrained language models, e.g.BERT, on the E2E-ABSA task.Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA.The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works.Besides, we also standardize the comparative study by consistently utilizing a hold-out development dataset for model selection, which is largely ignored by previous works.Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA. 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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
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
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
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Pre-trained models for natural language processing: A survey
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1647-3 · Cited by 1,591
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024) · ACM Computing Surveys · doi:10.1145/3605943 · Cited by 1,387
Abusive Bangla comments detection on Facebook using transformer-based deep learning models
Aurpa, T. T., Sadik, R., & Ahmed, M. S. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00852-x · Cited by 91
Sha, L., Rakovic, M., Das, A., Gasevic, D., & Chen, G. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3196278 · Cited by 67
AL-Smadi, M., Hammad, M. M., Al-Zboon, S. A., AL-Tawalbeh, S., & Cambria, E. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107540 · Cited by 62
Metadata
- Authors
- Li, X., Bing, L., Zhang, W., & Lam, W.
- Venue
- Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
- Published
- 2019
- Pages
- 34-41
- OpenAlex
- W2985056549
- Cited by
- 348
- References
- 68
- FWCI
- 15.33
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Li, X., Bing, L., Zhang, W., & Lam, W. (2019). Exploiting BERT for End-to-End Aspect-based Sentiment Analysis. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019). https://doi.org/10.18653/v1/d19-5505
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