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<Paper uid="A00-1022">
  <Title>Message Classification in the Call Center</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Customer care in technical domains is increasingly based on e-mail communication, allowing for the reproduction of approved solutions. For a call center agent, identifying the customer's problem is often 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.</Paragraph>
    <Paragraph position="1"> 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 answers. 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.</Paragraph>
    <Paragraph position="2"> Given that free text about arbitrary topics must be processed, in-depth approaches to language understanding 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 &amp;quot;relearning&amp;quot;.</Paragraph>
    <Paragraph position="3"> This paper describes a new approach to the classification 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 used in a commercial setting. Section 2 describes important properties of the input data, i.e. the e-mail texts on the one hand, and the categories on the other. These properties influenced the system architecture, which is presented in Section 3. Various publicly available SML systems have been tested with different methods of STP-based preprocessing.</Paragraph>
    <Paragraph position="4"> Section 4 describes the results. The implementation and usage of the system including the graphical user interface is presented in Section 5. We conclude by giving an outlook to further expected improvements (Section 6).</Paragraph>
  </Section>
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