Mining customer care dialogs for "Daily News"

Douglas, Shona; Agarwal, Deepak; Alonso, Tirso; Bell, Robert M.; Gilbert, Mazin; Swayne, Deborah F.; Volinsky, Chris

doi:10.1109/tsa.2005.851878

Cited by 19 publications

(14 citation statements)

References 4 publications

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“…Automatic Call-type Classification: A lot of work on automatic call type classification for the purpose of call routing ( [10], [7]), obtaining call log summaries [5], agent assisting and monitoring [11] has appeared in the past. In most cases, authors have modeled this as a text classification problem.…”

Section: Extraction Of Discourse Patternsmentioning

confidence: 99%

“…In [18], phrase level significance was obtained for noisy transcribed data where the phrases are clustered and combined into finite state machines. Other approaches use n-gram features with stop word removal and minimum support ( [10] [5]). …”

Section: Extraction Of Discourse Patternsmentioning

confidence: 99%

“…This discourse fragment is an instance corresponding to the most common discourse pattern that our algorithm extracts from the data. where tokens ending with "QUERY" are question clusters and DATE-ENTITY is the annotation for date (3rd November) 5 . This discourse pattern defines an instance of a discourse in which GREETING-QUESTION (e.g.…”

Section: Examples Of Important Discourse Patternsmentioning

confidence: 99%

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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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Section: Extraction Of Discourse Patternsmentioning

confidence: 99%

Section: Extraction Of Discourse Patternsmentioning

confidence: 99%

Section: Examples Of Important Discourse Patternsmentioning

confidence: 99%

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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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“…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. Segmentation of conversation transcripts has been attempted in the past [7] for information retrieval and summarization [8].…”

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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“…Spoken Dialog Systems (SDS) are now deployed on a large scale for full or partial automation in call-centers such as customer care services [1,2,3]. Such applications are usually deployed thanks to an initial training corpus of limited size, acquired by various wizard-ofoz techniques [4].…”

Section: Introductionmentioning

confidence: 99%

Speaker turn characterization for spoken dialog system monitoring and adaptation

Damnati

Béchet

Mori

2008

2008 IEEE Spoken Language Technology Workshop

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This paper describes an utterance classification method based on a multiple decoding scheme. We use the Spoken Language Understanding (SLU) strategy proposed within the European project LUNA. The goal of this classification process is to characterize each speaker's turn, in a dialog context, according to different categories relevant from an SLU point of view: out-of-domain messages, requests not covered by the interpretation module, frequent requests,. . . . These categories are used for two purposes in an off-line mode: system monitoring for detecting changes in users' behaviour and system adaptation by selecting dialogs likely to contain some phenomenon poorly covered by the models for an active learning scheme. All the models and the evaluations are performed on the France Telecom FT3000 corpus.

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Mining customer care dialogs for "Daily News"

Cited by 19 publications

References 4 publications

Identification of class specific discourse patterns

Identification of class specific discourse patterns

Mining conversational text for procedures with applications in contact centers

Speaker turn characterization for spoken dialog system monitoring and adaptation

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