Eichler, KathrinMeisdrock, MatthiasSchmeier, Sven2018-01-082018-01-0820122012https://dl.gi.de/handle/20.500.12116/11312Customer support departments of large companies are often faced with large amounts of customer requests about the same issue. These requests are usually answered by using preformulated text blocks. However, choosing the right text from a large number of text blocks can be challenging for the customer support agent, especially when the text blocks are thematically related. Optimizing this process using the power of language and knowledge technologies can save resources and improve customer satisfaction. We present a joint project between OMQ GmbH (www.omq.de) and the Language Technology lab of the DFKI GmbH (www.dfki.de) (German Research Center for Artificial Intelligence), in which, starting from the customer support system developed by OMQ, we addressed two major challenges: First, the classification of incoming customer requests into previously defined problem cases; second, the identification of new problem cases in a set of unclassified customer requests. The two tasks were approached using linguistic and statistical methods combined with machine learning techniques.Customer supportFuzzy searchTopic detectionText/Journal Article1610-1987