J08-1003 ranking of the six treebank-based LFG parsing systems . In order to carry out
N04-1013 feature-functions we use for stochastic LFG parsing see Riezler et al. ( 2002 ) .
J08-1002 for probabilistic modeling of LFG parsing . However , similarly to the
J07-4004 similar method in the context of LFG parsing ; an implementation is described
C94-1081 for making awfilablc to us their LFG parsing system . We would also like to
E93-1066 a number of applications like LFG parsing , ATN parsing and semantics analysis
J12-4006 high-performance platform for LFG parsing and generation . More information
W04-1510 two candidate c-structures in LFG parsing , but one is ill-formed semantically
J08-1003 algorithm and treebank - based LFG parsing architectures , we do not claim
J08-1003 evaluate a number of treebank-induced LFG parsing systems against the automatically
W00-1600 been exploring in our work on LFG parsing . Instead of taking the exponential
J08-1003 choose the best treebank-based LFG parsing system for the comparisons with
N06-1019 training method for a log-linear LFG parsing model in which the " correct
J13-1005 presented a reranker for German LFG parsing , and Dreyer , Smith , and Smith
W01-1018 deep language processing ( e.g. LFG parsing which is fast but inaccurate
J08-1002 and Curran 2003 , 2004b ) and LFG parsing ( Kaplan et al. 2004 ; Riezler
N13-1126 Dredze et al. , 2009 ) , log-linear LFG parsing ( Riezler et al. , 2002 ) , as
H05-1064 2002 ) describe a discriminative LFG parsing model that is trained on standard
N04-1013 in polynomial time even though LFG parsing is known to be an NP-complete
J08-1003 choose the best treebank-based LFG parsing systems for the PARC 700 and
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