C02-1130 examine adapting the hierarchical decision list algorithm from ( Yarowsky , 2000 ) to our
W02-1027 conducted several experiments with a decision list algorithm to explore the usefulness of
W02-1027 Discussion We have tested the decision list algorithm on our annotated corpus , employing
S01-1028 a feature set suitable for the Decision List algorithm . We detected some reasons that
S01-1028 , we will briefly explain the Decision List algorithm in Section 2 . Section 3 will
W99-0613 shares some characteristics of the decision list algorithm presented in this paper . ( Riloff
W02-1003 addition to comparing the various decision list algorithms , we also tried several other
P95-1026 illustrated in Figure 3 ) . The decision list algorithm resolves any conflicts by using
C02-1112 grammatical feature sets as used by the decision list algorithm . Instantiated Grammatical Relations
W02-1003 classifiers are more accurate than the decision list algorithms . Many of the problems that probabilistic
P94-1013 determined whether the corpus or decision list algorithm was correct in two cases of disagreement
P06-2022 instance , motivating his use of the decision list algorithm . In contrast , the goal here
S01-1028 integrated all the features in the Decision List algorithm , expecting that the most informative
W02-1003 the problems that probabilistic decision list algorithms have been used for are very similar
P95-1026 supervised training using the decision list algorithm , applied to the same data and
S01-1028 the system , expecting that the Decision List algorithm would be powerful enough to choose
S01-1028 BCU-dlist-ehu-all We trained our Decision List algorithm using local and global features
P94-1013 study ( Yarowsky , 1994 ) , the decision list algorithm outperformed both an N-Gram tagger
W02-1027 verb " face " ) . To compare our decision list algorithm role - for-verb + role to a selectional
D09-1048 classifiers , e.g. , semisupervised decision list algorithm ( Yarowsky , 1995 ) and Hyperlex
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