Natural Language Processing (almost) from Scratch
  We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements. 
Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray   Kavukcuoglu, Pavel Kuksa
cs.LG cs.CL