Abstract
Automatic call routing is one of the most important issues in the call center domain. It can be modeled –once performed the speech recognition of utterances– as a text classification task. Nevertheless, in this case, texts are extremely small (just a few words) and there are a great number of narrow call-type classes. In this paper, we propose a text classification method specially suited to work on this scenario. This method considers a new weighting scheme of terms and uses a multiple stage classification approach with the aim of balance the rate of rejected calls (directed to a human operator) and the classification accuracy. The proposed method was evaluated on a Spanish corpus consisting of 24,638 call utterances achieving outstanding results: 95.5% of classification accuracy with a rejection rate of just 8.2%.
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Uceda-Ponga, F., Villaseñor-Pineda, L., Montes-y-Gómez, M., Barbosa, A. (2008). A Misclassification Reduction Approach for Automatic Call Routing. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_17
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DOI: https://doi.org/10.1007/978-3-540-88636-5_17
Publisher Name: Springer, Berlin, Heidelberg
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