Greedy Transition-Based Dependency Parsing with Discrete and Continuous Supertag Features
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We study the effect of rich supertag features in greedy transition-based dependency parsing. While previous studies have shown that sparse boolean features representing the 1-best supertag of a word can improve parsing accuracy, we show that we can get further improvements by adding a continuous vector representation of the entire supertag distribution for a word. In this way, we achieve the best results for greedy transition-based parsing with supertag features with 88.6% LAS and 90.9% UASon the English Penn Treebank converted to Stanford Dependencies.
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