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.
Recently, "deep" Neural Nets (NN) have shown their superior performance in many natural language processing tasks including POS tagging, chunking, named entity recognition and semantic role labelling REF without any parametrization.
Natural Language Processing (almost) from Scratch
ArXiv
ArXiv
Mathematics Computer Science