SenseClusters - finding clusters that represent word senses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
| Hi-index | 0.00 |
Protecting sensitive information while preserving the share-ability and usability of data is becoming increasingly important. In call-centers a lot of customer related sensitive information is stored in audio recordings. In this work, we address the problem of protecting sensitive information in audio recordings and speech transcripts. We present a semi-supervised method to model sensitive information as a directed graph. Effectiveness of this approach is demonstrated by applying it to the problem of detecting and locating credit card transaction in real life conversations between agents and customers in a call center.