Neural Probabilistic Model for Non-projective MST Parsing

Understand

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.