DocumentCode
2769064
Title
Call classification for automated troubleshooting on large corpora
Author
Evanini, Keelan ; Suendermann, David ; Pieraccini, Roberto
Author_Institution
Univ. of Pennsylvania, Philadelphia
fYear
2007
fDate
9-13 Dec. 2007
Firstpage
207
Lastpage
212
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ASRU.2007.4430110
Filename
4430110
Link To Document