A Fast and Accurate Dependency Parser using Neural Networks
Danqi Chen, Christopher D. Manning
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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- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]→A Fast and Accurate Dependency Parser using Neural NetworksLegacy edge — source detail unavailable
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→A Fast and Accurate Dependency Parser using Neural NetworksLegacy edge — source detail unavailable
- Automatic differentiation in machine learning: a survey→A Fast and Accurate Dependency Parser using Neural NetworksLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Feature-rich part-of-speech tagging with a cyclic dependency networkLegacy edge — source detail unavailable
- A Fast and Accurate Dependency Parser using Neural Networks→Generating Typed Dependency Parses from Phrase Structure ParsesLegacy edge — source detail unavailable
- Natural Language Processing (almost) from Scratch→Feature-rich part-of-speech tagging with a cyclic dependency networkLegacy edge — source detail unavailable
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
- Natural Language Processing (almost) from Scratch→Feature-rich part-of-speech tagging with a cyclic dependency networkLegacy edge — source detail unavailable
- Automatic differentiation in machine learning: a survey→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Distributed Representations of Words and Phrases and their CompositionalityLegacy edge — source detail unavailable
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF→Natural Language Processing (almost) from ScratchLegacy edge — source detail unavailable
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| Natural Language Processing Techniques | Computer Science |
| Topic Modeling | Computer Science |
| Machine Learning in Bioinformatics | Biochemistry, Genetics and Molecular Biology |
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