The Startup
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The Startup

Building a Neural graph-based Dependency Parser

We implement a neural graph-based dependency parser inspired by those of Kiperwasser and Goldberg, 2016 [2] and Dozat and Manning, 2017 [3]. We train and test our parser on the English and Hindi Treebanks from the Universal Dependencies Project, achieving an unlabeled attachment score (UAS) of 84.80% and a labeled attachment score (LAS) of 78.61% on the English corpus, and a UAS of 91.92% and an LAS of 83.94% on the Hindi corpus. Note: Our UAS scores might be slightly inflated, since the arc from the root to itself always appears in both the prediction and training set. In…

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