The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Question Answering over Freebase with Multi-Column Convolutional Neural Networks
Open accessAbstract
Li Dong, Furu Wei, Ming Zhou, Ke Xu. 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.
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Cites
(2014) · Cited by 33,419
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
(2010) · Cited by 8,261
Natural Language Processing (almost) from Scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 4,962
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852
Practical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259
(2010) · Cited by 1,906
Semantic Parsing on Freebase from Question-Answer Pairs
Berant, J., Chou, A., Frostig, R., & Liang, P. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1160 · Cited by 1,517
Li, X., & Roth, D. (2002) · Proceedings of the 19th international conference on Computational linguistics - · doi:10.3115/1072228.1072378 · Cited by 1,234
Identifying Relations for Open Information Extraction
Fader, A., Soderland, S. G., & Etzioni, O. (2011) · Cited by 1,144
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Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
Visual Translation Embedding Network for Visual Relation Detection
Zhang, H., Kyaw, Z., Chang, S.-F., & Chua, T.-S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.331 · Cited by 586
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Metadata
- Authors
- Dong, L., Wei, F., Zhou, M., & Xu, K.
- Venue
- 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)
- Published
- 2015
- Pages
- 260-269
- OpenAlex
- W2251287417
- Cited by
- 445
- References
- 27
- FWCI
- 26.70
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Dong, L., Wei, F., Zhou, M., & Xu, K. (2015). Question Answering over Freebase with Multi-Column Convolutional Neural Networks. 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). https://doi.org/10.3115/v1/p15-1026
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
