D09-1072 acquisition with the learning of a POS disambiguation model . Moreover , the disambiguation
P07-1094 ) . All of this work is really POS disambiguation : learning is strongly constrained
J02-1004 lexical patterns as a context for POS disambiguation . As mentioned earlier , because
D09-1072 Simple Unsupervised Learner for POS Disambiguation Given Only a Minimal Lexicon
D15-1273 categorial method . As with the PoS disambiguation task , the topological method
J02-1004 into morphological analysis and POS disambiguation systems . POS disambiguation
E14-3013 error types , we should perform PoS disambiguation since the same surface form could
N01-1015 - verbs , etc. . The need for POS disambiguation is even clearer for languages
J02-1004 linguistic contexts necessary for POS disambiguation . Also , approaches using only
E99-1018 for future research . <title> POS Disambiguation and Unknown Word Guessing with
P02-1056 After morphological processing , POS disambiguation rules are applied which compute
E99-1018 achieving -- 5,5 % error rate for POS disambiguation and -- 16 % error rate for unknown
P07-1094 hope to unify the problems of POS disambiguation and syntactic clustering by presenting
C02-1071 ) andusethatinforma - tion for PoS disambiguation . This interaction of PoS disambiguation
E06-1034 tagging distinctions affect the POS disambiguation task , in section 5 we modify
P06-1041 sequences are anal - ysed and POS disambiguation is part of the task to be solved
J02-1004 and POS disambiguation systems . POS disambiguation has usually been performed by
J02-1004 statistical and rule-based approaches to POS disambiguation and can be tightly coupled with
P06-2045 approach was used to solve the POS disambiguation and unknown word guessing in
D09-1072 the unsupervised learning of a POS disambiguation model , and in most previous
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