Vector-based Natural Language Call Routing

Vector-based Natural Language Call Routing (1999)

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by Jennifer Chu-carroll , Bob Carpenter

Venue:	Computational Linguistics
Citations:	61 - 3 self

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Datum	Value	Source
TITLE	Vector-based Natural Language Call Routing	user correction - Legacy Corrections
AUTHOR NAME	Jennifer Chu-carroll	SVM HeaderParse 0.1
AUTHOR AFFIL	Lucent Technologies Bell Laboratories	SVM HeaderParse 0.2
AUTHOR NAME	Bob Carpenter	SVM HeaderParse 0.1
AUTHOR AFFIL	Lucent Technologies Bell Laboratories	SVM HeaderParse 0.2
ABSTRACT	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 vectorbased information retrieval techniques. Terms consist of n-gram sequences of morphologically reduced content words, while documents representing routing destinations consist 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 destinations with which it is consistent, based on our extension of the vector model. Evaluation of the call router performance over a financial services call center using both accurate transcriptions of calls and fairly noisy speech recognizer output demonstrated robustness in the face of speech recognition errors. More specifically, using accurate transcriptions of speech input, our system correctly routed 93.8% of the calls after redirecting 10.2% of all calls to a human operator. Using speech recognizer output with a 23% error rate reduced the number of correctly routed calls by 4%	user correction - Legacy Corrections
YEAR	1999	INFERENCE
VENUE	Computational Linguistics	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	361--388	INFERENCE
VOLUME	25	INFERENCE
CITATIONS	18 found	ParsCit 1.0