Thus rather than a single training procedure , we can actually partition the examples by predicate , and train a 1For a fixed verb , MI is proportional to Keller and Lapata ( 2003 ) 's conditional probability scores for pseudodisambiguation of ( v , n , n â² ) triples : Pr ( v | n ) = Pr ( v , n ) / Pr ( n ) , which was shown to be a better measure of association than co-occurrence frequency f ( v , n ) .
using the web to obtain frequencies for unseen bigrams
Frank Keller Mirella Lapata
In this representation , features for one predicate will be completely independent from those for every other predicate . Thus rather than a single training procedure , we can actually partition the examples by predicate , and train a 1For a fixed verb , MI is proportional to Keller and Lapata ( 2003 ) 's conditional probability scores for pseudodisambiguation of ( v , n , n â² ) triples : Pr ( v | n ) = Pr ( v , n ) / Pr ( n ) , which was shown to be a better measure of association than co-occurrence frequency f ( v , n ) . Normalizing by Pr ( v ) ( yielding MI ) allows us to use a constant threshold across all verbs .
Thus rather than a single training procedure , we can actually partition the examples by predicate , and train a 1For a fixed verb , MI is proportional to @@CITATION 's conditional probability scores for pseudodisambiguation of ( v , n , n â² ) triples : Pr ( v | n ) = Pr ( v , n ) / Pr ( n ) , which was shown to be a better measure of association than co-occurrence frequency f ( v , n ) .