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Neural Joint Model for Transition based Chinese Syntactic Analysis

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Figure

Figure 1: Transition-based Chinese joint modelfor word segmentation, POS tagging and depen-dency parsing.
Figure 2: The feed-forward neural network model.The greedy output is obtained at the second toplayer, while the beam decoding output is obtainedat the top layer
Table 2: Features for the joint model. “q0” denotesthe last shifted word and “q1” denotes the worddenotes the end character of the word
Table 3: Features for the bi-LSTM models. Allfeatures are words and characters. We experimentboth four and eight features models.
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