Since chunking can be performed efficiently and accurately, it is attractive to use it as a preprocessing step in full parsing stages. We analyze whether providing chunk data to a statistical dependency parser can benefit its accuracy. We present a set of experiments meant to select first a set of features that provide the greates improvement to a Shift/Reduce dependency parser, then to determine an appropriate feature model. We report on accuracy gain obtained using features from chunks produced using a statistical chunker as well as from an approximate representation of noun phrases induced directly by the parser. Finally we analyze the degree of accuracy that such a parser can achieve in chunking compared to a specialized statistical chunker.
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