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It has recently been shown that different NLP models can be effectively combined using dual decomposition.In this paper we demonstrate that PCFG-LA parsing models are suitable for combination in this way.We experiment with the different models which result from alternative methods of extracting a grammar from a treebank (retaining or discarding function labels, left binarization versus right binarization) and achieve a labeled Parseval F-score of 92.4 on Wall Street Journal Section 23 -this represents an absolute improvement of 0.7 and an error reduction rate of 7% over a strong PCFG-LA product-model baseline.Although we experiment only with binarization and function labels in this study, there is much scope for applying this approach to other grammar extraction strategies.
Roux et al. (Tue,) studied this question.
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