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• W2570781790 abstract "In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM and CNN. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. We evaluate our model on 17 different datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straight-forward end-to-end model training procedure via back-propagation. Our parser achieves state-of-the-art parsing performance on nine datasets." @default.
• W2570781790 created "2017-01-13" @default.
• W2570781790 creator A5060225743 @default.
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• W2570781790 date "2017-01-04" @default.
• W2570781790 modified "2023-09-27" @default.
• W2570781790 title "Neural Probabilistic Model for Non-projective MST Parsing" @default.
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• W2570781790 hasPublicationYear "2017" @default.
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