Automatic generation of domain models for call centers from noisy transcriptions

Roy, Shourya; Subramaniam, L. Venkata

doi:10.3115/1220175.1220268

Cited by 36 publications

(22 citation statements)

References 10 publications

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“…Traditional approach to building domain models is that the analysts manually generate a domain model through inspection of the call records. However, it has recently been proposed to use an unsupervised technique to generate domain models automatically from call transcriptions (Roy and Subramaniam, 2006). In addition, there has been research on how to automatically learn models of taskoriented discourse structure using dialog act and task information (Bangalore et al, 2006).…”

Section: Related Workmentioning

confidence: 99%

“…These knowledge sources contain task model, domain model, and agenda which are powerful representation to reflect the hierarchy of natural dialog control. In the spoken dialog systems, these are manually designed for various purposes including dialog modeling (Bohus andRudnicky, 2003, Lee et al, 2008), search space reduction , domain knowledge (Roy and Subramaniam, 2006), and user simulation .…”

Section: Introductionmentioning

confidence: 99%

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Automatic agenda graph construction from human-human dialogs using clustering method

Lee

Jung

Kim

et al. 2009

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Com

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Various knowledge sources are used for spoken dialog systems such as task model, domain model, and agenda. An agenda graph is one of the knowledge sources for a dialog management to reflect a discourse structure. This paper proposes a clustering and linking method to automatically construct an agenda graph from human-human dialogs. Preliminary evaluation shows our approach would be helpful to reduce human efforts in designing prior knowledge.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic agenda graph construction from human-human dialogs using clustering method

Lee

Jung

Kim

et al. 2009

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Com

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show abstract

“…Such information can be used to aid the underperforming agent (an agent who takes a longer time to resolve an issue than usual) to understand which among the steps or loops in his usual sequence could be avoided to improve efficiency. Clustering of call center dialogs has been employed to learn about similar dialog traces [2,3]. Automatically assigning quality scores to calls in contact centers [4], mining call transcripts for trend analysis [5] and call-flow based analysis of call center transcripts [6] are interesting research topics in the contact center analysis.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Mining conversational text for procedures with applications in contact centers

Deepak¹,

Kummamuru²

2007

IJDAR

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Many organizations provide dialog-based support through contact centers to sell their products, handle customer issues, and address product-and service-related issues. This is usually provided through voice calls-of late, web-chat based support is gaining prominence. In this paper, we consider any conversational text derived from web-chat systems, voice recognition systems etc., and propose a method to identify procedures that are embedded in the text. We discuss here how to use the identified procedures in knowledge authoring and agent prompting. In our experiments, we evaluate the utility of the proposed method for agent prompting. We first cluster the call transcripts to find groups of conversations that deal with a single topic. Then, we find possible procedure-steps within each topic-cluster by clustering the sentences within each of the calls in the topiccluster. We propose a measure for differentiating between clusters that are procedure-steps and those that are topical sentence collections. Once we identify procedure-steps, we represent the calls as sequences of procedure-steps and perform mining to extract distinct and long frequent sequences which represent the procedures that are typically followed in calls. We show that the extracted procedures are comprehensive enough. We outline an approach for retrieving relevant procedures for a partially completed call and illustrate the utility of distinct collections of sequences in the real-world scenario of agent prompting using the retrieval mechanism.This work is an extension of [16].

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“…Call Center Analytics: Call transcripts have been analyzed for topic classification [6], quality classification [20] and for estimating domain specific importance of call fragments [14]. It has also been shown that useful business intelligence can be obtained from customer agent conversations [16].…”

Section: Background and Related Workmentioning

confidence: 99%

Identification of class specific discourse patterns

Chalamalla

Negi

Subramaniam

et al. 2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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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.

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Automatic generation of domain models for call centers from noisy transcriptions

Cited by 36 publications

References 10 publications

Automatic agenda graph construction from human-human dialogs using clustering method

Automatic agenda graph construction from human-human dialogs using clustering method

Mining conversational text for procedures with applications in contact centers

Identification of class specific discourse patterns

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