Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Text analysis and knowledge mining system
IBM Systems Journal
Mining periodic patterns with gap requirement from sequences
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Automatic analysis of call-center conversations
Proceedings of the 14th ACM international conference on Information and knowledge management
Automatic generation of domain models for call centers from noisy transcriptions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Detection of question-answer pairs in email conversations
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Mining conversational text for procedures with applications in contact centers
International Journal on Document Analysis and Recognition
A survey of types of text noise and techniques to handle noisy text
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Journal of Intelligent Information Systems
Data-based research at IIT Bombay
ACM SIGMOD Record
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In this paper we address the problem of extracting important (and unimportant) discourse patterns from call center conversations. Call centers provide dialog based calling-in support for customers to address their queries, requests and complaints. A Call center is the direct interface between an organization and its customers and it is important to capture the voice-of-customer by gathering insights into the customer experience. We have observed that the calls received at a call center contain segments within them that follow specific patterns that are typical of the issue being addressed in the call. We present methods to extract such patterns from the calls. We show that by aggregating over a few hundred calls, specific discourse patterns begin to emerge for each class of calls. Further, we show that such discourse patterns are useful for classifying calls and for identifying parts of the calls that provide insights into customer behaviour.