P10-1012 the scores for MT outputs using noun-phrase chunking . Conse - quently , the system
P10-1012 To confirm the effectiveness of noun-phrase chunking , we performed the experiment
P10-1012 confirmed that our method using noun-phrase chunking is effective for automatic evaluation
P15-2086 cross-domain part-ofspeech tagging and noun-phrase chunking tasks . The experimental results
D11-1141 fundamental NLP tasks -- POS tagging and noun-phrase chunking . We also discuss a novel capitalization
P00-1023 tokenization , partof-speech tagging and noun-phrase chunking . The sentence detector we use
N06-1012 - tities . Our second task is noun-phrase chunking . We use the standard CoNLL 2000
P10-1012 for machine translation using noun-phrase chunking . Our method correctly determines
N03-1028 performance as good as any reported base noun-phrase chunking method on the CoNLL task , and
P10-1012 automatic evaluation method using noun-phrase chunking to obtain higher sentence-level
H05-1094 of part-of-speech tagging and noun-phrase chunking . Also , Carreras and Marquez
P00-1023 in Ratnaparkhi ( 1996 ) . The noun-phrase chunking is also done as a tagging task
W11-0207 lower performance of MetaMap for noun-phrase chunking compared to other tools . This
P10-1012 Automatic Evaluation Method using Noun-Phrase Chunking The system based on our method
W11-0207 tagging and showed that for both noun-phrase chunking and verb-phrase chunking , OpenNLP
P08-1074 et al. , 2006 ) and Portuguese noun-phrase chunking ( dos Santos and Oliveira , 2005
P10-1130 tokenization , CJK seg - mentation , noun-phrase chunking , and ( until now ) parsing --
P10-1012 indicate that our method using noun-phrase chunking is effective for some methods
W11-0207 relying on external tools for noun-phrase chunking . We evaluate these tools on
J14-1004 divergences . 4.4 Domain Adaptation for Noun-Phrase Chunking and Chinese POS Tagging We test
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