Projecting the Knowledge Graph to Syntactic Parsing
نویسندگان
چکیده
We present a syntactic parser training paradigm that learns from large scale Knowledge Bases. By utilizing the Knowledge Base context only during training, the resulting parser has no inference-time dependency on the Knowledge Base, thus not decreasing the speed during prediction. Knowledge Base information is injected into the model using an extension to the Augmented-loss training framework. We present empirical results that show this approach achieves a significant gain in accuracy for syntactic categories such as coordination and apposition.
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