Complex Temporal Patterns Detection over Continuous Data Streams
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Better punctuation prediction with dynamic conditional random fields
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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This paper presents an idea and first results of sentence modality classifier for Czech based purely on intonational information. This is in contrast with other studies which usually use more features (including lexical features) for this type of classification. As the sentence melody (intonation) is the most important feature, all the experiments were done on an annotated sample of Czech audiobooks library recorded by Czech leading actors. A non-linear model implemented by artificial neural network (ANN) was chosen for the classification. Two types of ANN are considered in this work in terms of temporal pattern classifications - classical multi-layer perceptron (MLP) network and Elman's network, results for MLP are presented. Pre-processing of temporal intonational patterns for use as ANN inputs is discussed. Results show that questions are very often misclassified as statements and exclamation marks are not detectable in current data set.