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Does it have to be trees? : Data-driven dependency parsing with incomplete and noisy training data

Spreyer, KathrinUniversität Potsdam

We present a novel approach to training data-driven dependency parsers on incomplete annotations. Our parsers are simple modifications of two well-known dependency parsers, the transition-based Malt parser and the graph-based MST parser. While previous work on parsing with incomplete data has …

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