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Natural language processing (NLP) has made incredible strides in the past few months. With these new possibilities and more and more textual data, we will see an increasing demand for NLP-based development. We have built several NLP-based systems in productive quality over the past eight years. It didn’t always go completely smoothly and the product didn’t always deliver what we wanted to achieve in the end. In this lecture we would like to report on our experiences: 1. What are the use cases of text analytics? 2. Which specific information can be extracted from text using NLP libraries? 3. What are the limits of text analytics? This talk should encourage to deal with NLP but at the same time slow down the current euphoria and expectations.
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