Customer care in technical domains is increasingly based on e-mail communication, allowing for the reproduction 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 classification of e-mail requests based on shallow text processing and machine learning techniques. It is implemented within an assistance system for call center agents that is used in a commercial setting.

Appointment scheduling is a problem faced daily by many individuals and organizations. Cooperating agent systems have been developed to partially automate this task. In order to extend the circle of participants as far as possible we advocate the use of natural language transmitted by email. We describe Cosma, a fully implemented German language server for existing appointment scheduling agent systems. Cosma can cope with multiple dialogues in parallel, and accounts for differences in dialogue behaviour between human and machine agents. NL coverage of the sublanguage is achieved through both corpusbased grammar development and the use of message extraction techniques.

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We present a mobile touchable application for online topic graph extraction and exploration of web content. The system has been implemented for operation on an iPad. The topic graph is constructed from N web snippets which are determined by a standard search engine. We consider the extraction of a topic graph as a specific empirical collocation extraction task where collocations are extracted between chunks. Our measure of association strength is based on the pointwise mutual information between chunk pairs which explicitly takes their distance into account. An initial user evaluation shows that this system is especially helpful for finding new interesting information on topics about which the user has only a vague idea or even no idea at all.

Monitoring mobility-and industryrelevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge. We present Spree, a scalable system for real-time, automatic event extraction from social media, news and domain-specific RSS feeds. Our system is tailored to a range of mobilityand industry-related events, and processes German texts within a distributed linguistic analysis pipeline implemented in Apache Flink. The pipeline detects and disambiguates highly ambiguous domain-relevant entities, such as street names, and extracts various events with their geo-locations. Event streams are visualized on a dynamic, interactive map for monitoring and analysis.