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Spoken Language Understanding via Supervised Learning and Linguistically Motivated Features

Publication date2010
Abstract

In this paper, we reduce the rescoring problem in a spoken dialogue understanding task to a classification problem, by using the semantic error rate as the reranking target value. The classifiers we consider here are trained with linguistically motivated features. We present comparative experimental evaluation results of four supervised machine learning methods: Support Vector Machines, Weighted K-Nearest Neighbors, Naïve Bayes and Conditional Inference Trees. We provide a quantitative evaluation of learning and generalization during the classification supervised training, using cross validation and ROC analysis procedures. The reranking is derived using the posterior knowledge given by the classification algorithms.

Citation (ISO format)
GEORGESCUL, Maria, RAYNER, Emmanuel, BOUILLON, Pierrette. Spoken Language Understanding via Supervised Learning and Linguistically Motivated Features. In: Proceedings of the 15th International Conference on Applications of Natural Language to Information Systems, 2010, p. 117–128. doi: 10.1007/978-3-642-13881-2_12
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Creation09/06/2010 5:19:00 PM
First validation09/06/2010 5:19:00 PM
Update time03/14/2023 4:06:19 PM
Status update03/14/2023 4:06:19 PM
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