Efficiency in Unification-Based N-Best Parsing

We extend a recently proposed algorithm for n-best unpacking of parse forests to deal efficiently with (a) Maximum Entropy (ME) parse selection models containing important classes of non-local features, and (b) forests produced by unification grammars containing significant proportions of globally inconsistent analyses. The new algorithm empirically exhibits a linear relationship between processing time and the number of analyses unpacked at all degrees of ME feature non-locality; in addition, compared with agenda-driven best-first parsing and exhaustive parsing with post-hoc parse selection it leads to improved parsing speed, coverage, and accuracy.

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