The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Fast and Accurate Dependency Parser using Neural Networks
Open accessAbstract
Almost all current dependency parsers classify based on millions of sparse indi-cator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed signif-icantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based depen-dency parser. Because this classifier learns and uses just a small number of dense fea-tures, it can work very fast, while achiev-ing an about 2 % improvement in unla-beled and labeled attachment scores on both English and Chinese datasets. Con-cretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank. 1
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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
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R., & Makhoul, J. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1129 · Cited by 481
Cited by
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Tai, K. S., Socher, R., & Manning, C. D. (2015) · 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) · doi:10.3115/v1/p15-1150 · Cited by 3,081
Self-citationRecent 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
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Ma, X., & Hovy, E. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1101 · Cited by 2,567
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
Brain intelligence: go beyond artificial intelligence
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018) · Mobile Networks and Applications · doi:10.1007/s11036-017-0932-8 · Cited by 1,153
Metadata
- Authors
- Chen, D., & Manning, C.
- Venue
- Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2014
- Pages
- 740-750
- OpenAlex
- W2250861254
- Cited by
- 1,795
- References
- 3
- FWCI
- 116.81
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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