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• W1491309538 abstract "Graph-based dependency parsing algorithms commonly employ features up to third order in an attempt to capture richer syntactic relations. However, each level and each feature combination must be defined manually. Besides that, input features are usually represented as huge, sparse binary vectors, offering limited generalization. In this work, we present a deep architecture for dependency parsing based on a convolutional neural network. It can examine the whole sentence structure before scoring each head/modifier candidate pair, and uses dense embeddings as input. Our model is still under ongoing work, achieving 91.6% unlabeled attachment score in the Penn Treebank." @default.
• W1491309538 created "2016-06-24" @default.
• W1491309538 creator A5036870275 @default.
• W1491309538 creator A5059202822 @default.
• W1491309538 date "2015-01-01" @default.
• W1491309538 modified "2023-10-17" @default.
• W1491309538 title "A Deep Architecture for Non-Projective Dependency Parsing" @default.
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• W1491309538 doi "https://doi.org/10.3115/v1/w15-1508" @default.
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