Title :
Call classification for automated troubleshooting on large corpora
Author :
Evanini, Keelan ; Suendermann, David ; Pieraccini, Roberto
Author_Institution :
Univ. of Pennsylvania, Philadelphia
Abstract :
This paper compares six algorithms for call classification in the framework of a dialog system for automated troubleshooting. The comparison is carried out on large datasets, each consisting of over 100,000 utterances from two domains: television (TV) and Internet (INT). In spite of the high number of classes (79 for TV and 58 for INT), the best classifier (maximum entropy on word bigrams) achieved more than 77% classification accuracy on the TV dataset and 81% on the INT dataset.
Keywords :
entropy; interactive systems; pattern classification; automated large corpora troubleshooting; call classification; dialog system; maximum entropy approach; Boosting; Cities and towns; Entropy; Hardware; Internet; Machine learning algorithms; Natural language processing; Problem-solving; Statistics; TV; automated troubleshooting; call classification; large corpora;
Conference_Titel :
Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-1746-9
Electronic_ISBN :
978-1-4244-1746-9
DOI :
10.1109/ASRU.2007.4430110
