The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Spoken Language System for Automated Call Routing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Integrating prosodic and lexical cues for automatic topic segmentation
Computational Linguistics
Vector-based natural language call routing
Computational Linguistics
Message classification in the call center
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Automatic glossary extraction: beyond terminology identification
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Automatic analysis of call-center conversations
Proceedings of the 14th ACM international conference on Information and knowledge management
Spoken document retrieval from call-center conversations
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using conditional random fields for sentence boundary detection in speech
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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
Semi-automated logging of contact center telephone calls
Proceedings of the 17th ACM conference on Information and knowledge management
Discovering customer intent in real-time for streamlining service desk conversations
Proceedings of the 20th ACM international conference on Information and knowledge management
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This paper presents a SVM (Support Vector Machine) classification system which divides contact-center call transcripts into "Greeting", "Question", "Refine", "Research", "Resolution", "Closing" and "Out-of-topic" sections. This call section segmentation is useful to improve search and retrieval functions and to provide more detailed statistics on calls. We use an off-the-shelf automatic speech recognition (ASR) system to generate call transcripts from recorded calls between customers and service representatives. We first classify an individual utterance into a call section by applying the SVM classifier and then merge adjacent utterances classified into a same call section. We experiment with the proposed system on 100 automatically transcribed calls. The 10-fold cross validation shows 87.2% classification accuracy. We also compare the proposed algorithm with two other approaches - the most frequent section only method and a maximum entropy-based segmentation. The evaluation shows that our system's accuracy is 12% higher than the first baseline system and 6% higher than the second baseline system respectively.