W05-0302 more semantically based task of event classification . On the other hand , the information
W09-1321 Localization ( GO0051179 ) . The event classifications used in the corpus are depicted
P09-2051 a framework for negative life event classification . We formulate this problem as
W09-1410 a classifier using our task 1 event classifications combined with the gold-standard
D10-1100 Classification For the social event classification task , we only consider pairs
W09-1704 clusters to adjust the likelihood of event classification . For example , in the following
P09-2051 significant features for negative life event classification . 5 Conclusion This work has
P09-2051 classifiers for negative life event classification ( Section 3 ) . Experimental
S15-2135 event extent identification and event classification tasks we use various features
P04-1074 attribute . 3 . The effect of event classification is striking . Taking this feature
D10-1100 Argument classification and 3 ) Event classification . Ji and Grishman ( 2008 ) further
D10-1100 oversampling techniques . 6.3 Social Event Classification For the social event classification
P09-2051 language patterns for negative life event classification . The association language patterns
E14-2017 type set , shown in Table 1 . The event classification module consists of a blend of
A83-1009 below . Before describing the event classification , however , It Is enlightening
P04-1074 restoration ( Yarowsky , 1994 ) and event classification ( Siegel and McKeown , 1998 )
C04-1101 restoration ( Yarowsky , 1994 ) and event classification ( Siegel , 1998 ) . Temporal
D10-1100 transformations . In contrast , the social event classification task does not suffer from data
D10-1100 social event detection and social event classification tasks . In the future , we will
A83-1009 of the model derives from the event classification schema mentioned above , and
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