Regular models of phonological rule systems
Computational Linguistics - Special issue on computational phonology
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Text speech translation by means of subsequential transducers
Extended finite state models of language
Finite state transducers: parsing free and frozen sentences
Extended finite state models of language
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Supertagging: an approach to almost parsing
Computational Linguistics
Compilation of weighted finite-state transducers from decision trees
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
An efficient compiler for weighted rewrite rules
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Dependency parsing with an extended finite state approach
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Stochastic finite-state models for spoken language machine translation
NAACL-ANLP-EMTS '00 Proceedings of the 2000 NAACL-ANLP Workshop on Embedded machine translation systems - Volume 5
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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.