Message Classification in the Call Center

Message Classification in the Call Center Stephan Busemann, Seen Schmeier~ Roman G. Arens DFKI GmbH Stuhlsatzenhausweg 3, D-66123 Saarbriicken, Germany e-mail: {busemann, schmeier, arens}@dfki.de Abstract Customer care in technical domains is increasingly based on e-mail communication, allowing for the re- production of approved solutions. Identifying the customer's problem is often time-consuming, as the problem space changes if new products are launched. This paper describes a new approach to the classifi- cation of e-mail requests based on shallow text pro- cessing and machine learning techniques. It is im- plemented within an assistance system for call center agents that is used in a commercial setting. 1 Introduction Customer care in technical domains is increasingly based on e-mail communication, allowing for the re- production of approved solutions. For a call cen- ter agent, identifying the customer's problem is of- ten time-consuming, as the problem space changes if new products are launched or existing regulations are modified. The typical task of a call center agent processing e-mail requests consists of the following steps: Recognize the problem(s): read and understand the e-mail request; Search a solution: identify and select predefined text blocks; Provide the solution: if necessary, customize text blocks to meet the current request, and send the text. This task can partly be automated by a system suggesting relevant solutions for an incoming e-mail. This would cover the first two steps. The last step can be delicate, as its primary goal is to keep the customer satisfied. Thus human intervention seems mandatory to allow for individual, customized an- swers. Such a system will • reduce the training effort required since agents don't have to know every possible solution for every possible problem; • increase the agents' performance since agents can more quickly select a solution among several offered than searching one; • improve the quality of responses since agents will behave more homogeneously - both as a group and over time - and commit fewer errors. Given that free text about arbitrary topics must be processed, in-depth approaches to language un- derstanding are not feasible. Given further that the topics may change over time, a top-down approach to knowledge modeling is out of the question. Rather a combination of shallow text processing (STP) with statistics-based machine learning techniques (SML) is called for. STP gathers partial information about text such as part of speech, word stems, negations, or sentence type. These types of information can be used to identify the linguistic properties of a large training set of categorized e-mails. SML techniques are used to build a classifier that is used for new, incoming messages. Obviously, the change of topics can be accommodated by adding new categories and e-mails and producing a new classifier on the basis of old and new data. We call this replacement of a classifier "relearning". This paper describes a new approach to the clas- sification of e-mail requests along these lines. It is implemented within the ICe-MAIL system, which is an assistance system for call center agents that is currently …

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