A97-1022 design of grammar for positive projective parsing . The core idea of this approach
E06-1011 projective parsers using the exact projective parsing algorithms . The purpose of these
E12-1042 si , sj ) Ey using the standard projective parsing algorithm for arc-factored models
E06-1011 to use a O ( n3 ) second-order projective parsing algorithm , as we will see later
E14-4031 uses 1st-order features , and a projective parsing algorithm with 5-best MIRA training
D14-1099 These oracles are all defined for projective parsing . In this paper , we present
D08-1017 approximation based on O ( n3 ) projective parsing followed by rearrangement to
D08-1016 raise the polynomial runtime of projective parsing , and render non-projective parsing
D09-1004 McDonald et al. , 2006 ) with projective parsing . Moreover , we exploit three
J08-4003 parsing technique to a strictly projective parsing algorithm . Moreover , despite
D14-1099 this enhancement is limited to projective parsing , and dynamic oracles have not
D11-1114 has been successfully used for projective parsing ( Huang and Sagae , 2010 ; Kuhlmann
D13-1152 iterations in our firstorder dynamic projective parsing . From iterations 1 to 6 , we
D13-1152 c ) , we can not afford to run projective parsing multiple times . The single resulting
D13-1152 runtime -- the same as one call to projective parsing , and far faster in prac - tice
E12-2012 projective version of Covington 's projective parsing algorithm and the projective
J08-4003 combination with an essentially projective parsing algo - rithm . Finally , we have
D13-1152 information via a coarse-to-fine projective parsing cas cade ( Charniak et al. ,
J08-4003 complexity is O ( n ) . ■ 5.2 Projective Parsing The transition set T for the
E06-1011 already been found by the exact projective parsing algorithm . It is not difficult
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