P06-1134 subjectivity on the quality of a word sense classifier . To answer this ques - tion
W01-0704 consisting of the building of word sense classifiers through training on a semantically
S01-1032 consisting of the building of word sense classifiers through training on a semantically
W11-1104 a portion of the corpus into a word sense classifier , which is then tested on the
P04-1038 and difficult for a supervised word sense classifier ( Dang et al. , 2002 ) . 9 In
S13-1003 senses of the same word . Training word sense classifiers for Levels 1 and 3 is straightforward
E14-4007 ontologies and we developed a vague word sense classifier using training data from Wordnet
W02-0810 information is used to obtain a second word sense classifier , used in system combination
N07-1025 suggests that the accuracy of the word sense classifiers built on this data is likely
J98-1005 the two networks into a single word sense classifier . While Veronis and Ide ( 1990
N07-1025 tagged corpora and build accurate word sense classifiers for a large number of languages
E14-4007 present in this paper a vague word sense classifier that may help both ontology creators
P13-1055 word-in-context classifiers into true word sense classifiers . Acknowledgments This work was
S07-1004 likelihoods of a Naïve Bayes word sense classifier not from senseannotated ( in
S07-1071 implemented an unsupervised naive Bayes word sense classifier using these DPCs that was best
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