Compiling Boostexter Rules into a Finite-state Transducer

A number of NLP tasks have been effectively modeled as classification tasks using a variety of classification techniques. Most of these tasks have been pursued in isolation with the classifier assuming unambiguous input. In order for these techniques to be more broadly applicable, they need to be extended to apply on weighted packed representations of ambiguous input. One approach for achieving this is to represent the classification model as a weighted finite-state transducer (WFST). In this paper, we present a compilation procedure to convert the rules resulting from an AdaBoost classifier into an WFST. We validate the compilation technique by applying the resulting WFST on a call-routing application.