Following previous work ( e.g. , Soon et al. ( 2001 ) and Ponzetto and Strube ( 2006 ) ) , we generate training instances as follows : a positive instance is created for each anaphoric NP , NPj , and its closest antecedent , NPi ; and a negative instance is created for NPj paired with each of the intervening NPs , NPi +1 , NPi +2 , ... , NPj_1 .
a machine learning approach to coreference resolution of noun phrases
W M Soon H T Ng D Lim
Our baseline coreference system uses the C4 .5 decision tree learner ( Quinlan , 1993 ) to acquire a classifier on the training texts for determining whether two NPs are coreferent . Following previous work ( e.g. , Soon et al. ( 2001 ) and Ponzetto and Strube ( 2006 ) ) , we generate training instances as follows : a positive instance is created for each anaphoric NP , NPj , and its closest antecedent , NPi ; and a negative instance is created for NPj paired with each of the intervening NPs , NPi +1 , NPi +2 , ... , NPj_1 . Each instance is represented by 33 lexical , grammatical , semantic , and
Following previous work ( e.g. , @@CITATION and Ponzetto and Strube ( 2006 ) ) , we generate training instances as follows : a positive instance is created for each anaphoric NP , NPj , and its closest antecedent , NPi ; and a negative instance is created for NPj paired with each of the intervening NPs , NPi +1 , NPi +2 , ... , NPj_1 .