The Philips automatic train timetable information system
Speech Communication - Special issue on interactive voice technology for telecommunication applications
Stochastic versus rule-based speech understanding for information retrieval
Speech Communication
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Combining statistical and knowledge-based spoken language understanding in conditional models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Statistical framework for a Spanish spoken dialogue corpus
Speech Communication
Arabic dialect processing tutorial
NAACL-Tutorials '07 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Tutorial Abstracts
Machine translation of Arabic dialects
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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In this paper, we propose to evaluate the performance of a discriminative model to semantically label spoken Tunisian dialect turns which are not segmented into utterances. We evaluate discriminative algorithm based on Conditional Random Fields (CRF). We check the performance of the CRF model to concept labeling on raw data in Tunisian dialect which are not analyzed in advance. We compared its performance with different types of preprocessing data until arriving to well treated data. CRF model showed the ability to ameliorate the accuracy of labeling task for spoken language understanding of not segmented and not treated speech in Tunisian dialect.