D09-1116 using off-the-shelf POS taggers or PCFG parsers . However , the amount of information
D12-1095 extracted from the chart of a base PCFG parser , usually employing a heavy pruning
D09-1087 improvements from self-training a PCFG parser on the standard WSJ training
D14-1100 classifier outperforms the current PCFG parser . Furthermore , it can be easily
C04-1104 application of the acquired lexicon to a PCFG parser indicates great potentialities
C04-1204 on other formalisms including PCFG parsers . At the same time , following
D14-1100 the same as the result of our PCFG parser because both systems use contextual
D11-1064 Since we do not have an a able PCFG parser as in ( Huang and Harper , 2009
C04-1204 still below the state-of-theart PCFG parsers ( Collins , 1999 ; Charniak ,
D14-1100 effectively integrated into the PCFG parser and general statistical parsing
D12-1096 parser ( - F ) , which combines the PCFG parser with a lexicalised dependency
D12-1095 Details We used the first-stage PCFG parser of Charniak and Johnson ( 2005
D14-1098 trained on Penn Treebank data . The PCFG parser is generalized to take the lattice
D09-1015 labeli_1 + labeli chart-based PCFG parser , except that instead of putting
D10-1050 stand-alone sentence , if the PCFG parser has marked i with an S ( sen
D12-1095 two parsers are state-of-the-art PCFG parsers for English and German , respectively
C04-1204 those parsers was still below PCFG parsers ( Collins , 1999 ; Charniak ,
C04-1204 analysis has not outperformed PCFG parsers in terms of the accuracy of surface
D09-1087 Charniak 's parser is a lexicalized PCFG parser that models lexicalized dependencies
D12-1096 consider both the unlexicalised PCFG parser ( - U ) and the factored parser
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