DFKI-LT - Eficiency in Unification-Based N-best Parsing

Yi Zhang, Stephan Oepen, John Carroll
Eficiency in Unification-Based N-best Parsing
in: Harry Bunt, Paola Merlo, Joakim Nivre (eds.):
1 Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing volume 43,
Text, Speech and Language Technology Series, Pages 223-241, Springer, 2010

 
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 uni- fication grammars containing significant proportions of globally inconsistent analy- ses. 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 ac- curacy.
 
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