To combine the phrasal matching scores obtained at each n-gram level , and optimize their relative weights , we trained a Support Vector Machine classifier , SVMlight ( Joachims , 1999 ) , using each score as a feature .
making largescale support vector machine learning practical
Thorsten Joachims
The phrasal matching score for each n-gram level is calculated as follows : To combine the phrasal matching scores obtained at each n-gram level , and optimize their relative weights , we trained a Support Vector Machine classifier , SVMlight ( Joachims , 1999 ) , using each score as a feature .
To combine the phrasal matching scores obtained at each n-gram level , and optimize their relative weights , we trained a Support Vector Machine classifier , SVMlight ( @@CITATION ) , using each score as a feature .