Automatic natural language understanding enables natural communication with computers and computer-assisted access to the content of large document collections. While classical approaches to artificial intelligence anticipate all possible situations and interactions in form of a fully specified dialogue model or ontology, they are hard to adapt to new domains and do not cope well with language change. In this talk, I will motivate an adaptive, purely data-driven approach to natural language processing. Illustrated by recent research prototypes, three stages of data-driven adaptation will be illustrated: feature/resource induction, induction of processing components and continuous data-driven learning. Finally, I will discuss current research and future directions regarding the integration of symbolic and statistical knowledge, interpretability of language processing components as well as advanced forms of information access.
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