Paper: Compiling Boostexter Rules Into A Finite-State Transducer

ACL ID P04-3021
Title Compiling Boostexter Rules Into A Finite-State Transducer
Venue Annual Meeting of the Association of Computational Linguistics
Session System Demonstration
Year 2004

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