Natural Language Processing (Almost) from Scratch
Collobert, Ronan (Creator), Weston, Jason (Creator), Bottou, Léon (Creator), Karlen, Michael (Creator), Kavukcuoglu, Koray (Creator), Kuksa, Pavel (Creator)
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
Downloadable Archival Material, English, 2011