These keywords are potentially useful features because some of them are subclasses of the ACE SCs shown in the left column of Table 1 , while others appear to be correlated with these ACE SCs .2 ( 6 ) INDUCED CLASS : Since the first-sense heuristic used in the previous feature may not be accurate in capturing the SC of an NP , we employ a corpusbased method for inducing SCs that is motivated by research in lexical semantics ( e.g. , Hearst ( 1992 ) ) .
automatic acquisition of hyponyms from large text corpora
M Hearst
( 5 ) WN CLASS : For each keyword w shown in the right column of Table 1 , we determine whether the head noun of NPZ is a hyponym of w in WordNet , using only the first WordNet sense of NPZ .1 If so , we create a WN CLASS feature with w as its value . These keywords are potentially useful features because some of them are subclasses of the ACE SCs shown in the left column of Table 1 , while others appear to be correlated with these ACE SCs .2 ( 6 ) INDUCED CLASS : Since the first-sense heuristic used in the previous feature may not be accurate in capturing the SC of an NP , we employ a corpusbased method for inducing SCs that is motivated by research in lexical semantics ( e.g. , Hearst ( 1992 ) ) . Given a large , unannotated corpus3 , we use IdentiFinder to label each NE with its NE type and MINIPAR to extract all the appositive relations .
These keywords are potentially useful features because some of them are subclasses of the ACE SCs shown in the left column of Table 1 , while others appear to be correlated with these ACE SCs .2 ( 6 ) INDUCED CLASS : Since the first-sense heuristic used in the previous feature may not be accurate in capturing the SC of an NP , we employ a corpusbased method for inducing SCs that is motivated by research in lexical semantics ( e.g. , @@CITATION ) .