However , learning-based resolvers have not been able to benefit from having an SC agreement feature , presumably because the method used to compute the SC of an NP is too simplistic : while the SC of a proper name is computed fairly accurately using a named entity ( NE ) recognizer , many resolvers simply assign to a common noun the first ( i.e. , most frequent ) WordNet sense as its SC ( e.g. , Soon et al. ( 2001 ) , Markert and Nissim ( 2005 ) ) .
a machine learning approach to coreference resolution of noun phrases
W M Soon H T Ng D Lim
Another type of semantic knowledge that has been employed by coreference resolvers is the semantic class ( SC ) of an NP , which can be used to disallow coreference between semantically incompatible NPs . However , learning-based resolvers have not been able to benefit from having an SC agreement feature , presumably because the method used to compute the SC of an NP is too simplistic : while the SC of a proper name is computed fairly accurately using a named entity ( NE ) recognizer , many resolvers simply assign to a common noun the first ( i.e. , most frequent ) WordNet sense as its SC ( e.g. , Soon et al. ( 2001 ) , Markert and Nissim ( 2005 ) ) . It is not easy to measure the accuracy of this heuristic , but the fact that the SC agreement feature is not used by Soon et al. 's decision tree coreference classifier seems to suggest that the SC values of the NPs are not computed accurately by this first-sense heuristic .
However , learning-based resolvers have not been able to benefit from having an SC agreement feature , presumably because the method used to compute the SC of an NP is too simplistic : while the SC of a proper name is computed fairly accurately using a named entity ( NE ) recognizer , many resolvers simply assign to a common noun the first ( i.e. , most frequent ) WordNet sense as its SC ( e.g. , @@CITATION , Markert and Nissim ( 2005 ) ) .