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Transition-based Spinal Parsing

Xavier Carreras, Miguel Ballesteros
We present a transition-based arc-eager model to parse spinal trees, a dependencybased representation that includes phrasestructure information in the form of constituent spines assigned to tokens. As a main advantage, the arc-eager model can utilize a rich set of features combining dependency and constituent information, while parsing in linear time. We describe a set of conditions for the arc-eager system to produce valid spinal structures. In experiments using beam search we show that the model obtains a good trade-off between speed and accuracy, and yields state of the art performance for both dependency and constituent parsing measures.
CoNLL 2015, Beijing, China, 30-31 July, 2015
2015
2015/029

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2015-029.pdf (190.41 kB)