Parsing with CYK over Distributed Representations: "Classical" Syntactic Parsing in the Novel Era of Neural Networks
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Syntactic parsing is a key task in natural language processing which has been dominated by symbolic, grammar-based syntactic parsers. Neural networks, with their distributed representations, are challenging these methods. In this paper, we want to show that existing parsing algorithms can cross the border and be defined over distributed representations. We then define D-CYK: a version of the traditional CYK algorithm defined over distributed representations. Our D-CYK operates as the original CYK but uses matrix multiplications. These operations are compatible with traditional neural networks. Experiments show that D-CYK approximates the original CYK. By showing that CYK can be performed on distributed representations, our D-CYK opens the possibility of defining recurrent layers of CYK-informed neural networks.
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