P00-1015 sentence is taken into account during baseNP identification . ( 3 ) Viterbi algorithm is
P00-1015 novel statistical approach to baseNP identification , which considers both steps
W07-1022 treebank complete anno - tation , but baseNP identification is probably a simpler task .
P00-1015 prior work treats POS tagging and baseNP identification as two separate procedures .
P00-1015 learned that the POS tagging and baseNP identification are influenced each other . We
P00-1015 improve the performances of both baseNP identification and POS tagging . 4 Comparison
P00-1015 have dealt with the problem of baseNP identification ( Church 1988 ; Bourigault 1992
P00-1015 Part-Of-Speech ( POS ) tagging and baseNP identification given the N-best POS-sequences
N01-1025 margin parameter to be 1 . In the baseNP identification task , the performance of the
W01-0712 chunking task is NP CHUNKING or baseNP identification in which the goal is to identify
P00-1015 characteristics : ∏ = ; 1 ( 1 ) baseNP identification is implemented in two related
W07-1022 application task . We also use the baseNP identification in order to type the occurrence
P00-1015 . Given E , the result of the baseNP identification is assumed to be a sequence ,
P00-1015 best sequences of POS tagging and baseNP identification . Before describing our algorithm
P00-1015 Our statistical model unifies baseNP identification and POS tagging through tracing
N01-1025 taken as the standard data set for baseNP identification task2 . This data set consists
P00-1015 that only one , the F-measure of baseNP identification is improved from 93.02 % to 93.07
P00-1015 knowledge , three other approaches to baseNP identification have been evaluated using Penn
P00-1015 unknown words are encountered during baseNP identification , we calculate parameter ( 2
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