It has been often noted that the limited attention paid by the machine learning community to text and speech data seems unjustified.
Lexical knowledge is often hard to compile by hand, and even harder to port and reuse.
NLP application systems still have a low impact on real world problems, mainly due to the costs related to reusability and customization of the required lexicons. In particular, changes in the domain causes changes in the lexical information required in the underlying natural language. Empirical, symbolic machine learning methods can be perfectly suited for this task like automatic acquisition and adaptation of this knowledge. This is what we mean with adaptive NLP-driven systems. The Workshop is expected to cover the whole day.