Vector-Based Natural Language Call Routing
01 September 1999
This paper describes a domain independent, automatically trained natural language call router for directing incoming calls in a call center. Our call router directs customer calls based on their response to an open-ended "How may I direct your call?" prompt. Routing behavior is trained from a corpus of transcribed and hand-routed calls and then carried out using vector-based information retrieval techniques. Terms consist of n-gram sequences of morphologically reduced content words, while documents representing routing destinations consists of weighted term frequencies derived from calls to that destination in the training corpus. Based on the statistical discriminating power of the n-gram terms extracted from the caller's request, the caller is 1) routed to the appropriate destination, 2) transferred to a human operator, or 3) asked a disambiguation question. In the last case, the system dynamically generates queries tailored to the caller's request and the destination with which it is consistent, based on our extension of the vector model. Evaluation of the call router performance over a financial service call center using both accurate transcriptions of calls and fairly noisy speech recognizer output demonstrated robustness in the face of speech recognition errors. Furthermore, our system showed a substantial improvement in performance over existing systems by correctly routing 93.8% of the calls after punting 10.2% of all calls to a human operator on transcription, with approximately 4% degradation in performance when using speech recognizer output with a 23% word error rate.