Abstract. 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.
In this paper, we address the problem of the morphological analysis of an Arabic dialect. We propose a method to adapt an Arabic morphological analyzer for the Tunisian dialect (TD). In order to do that, we create a lexicon for the TD. The creation of the lexicon is done in two steps. The first step consists in adapting a Modern Standard Arabic (MSA) lexicon. We adapted a list of MSA derivation patterns to TD. The second step consists in improving the resulting lists of patterns and roots by using TD specific roots and patterns. The proposed method has been tested and has achieved an Fmeasure performance of 88%.
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