I was wondering if one could use Reinforcement Learning (as it is going to be more and more trendy with the Google DeepMind & AlphaGo's stuff) to parse and extract information from text.
For example, could it be a competitive approach to structured prediction such as
Named Entity Recognition (NER), i.e. the task of labelling New York by "city", and New York Times by "organization" Part-of-speech tagging (POS), i.e. classifying words as determinant, noun, etc. information extraction, i.e. finding and labelling some target information in texts, for instance 12/03 is date given the context meaning 3 December and has the label "expiry date" What would be a relevant modelling to do these tasks?
Rather naively I would think of a pointer that read the text from start to end and annotate each 'letter' by a label. Maybe it would learn that neighbouring letters in a 'word' share the same label, etc. Would it be able to learn long-term dependencies with this approach?
I am interested by any ideas or references related to this subject.