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.

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@article{Collobert2011NaturalLP, title={Natural Language Processing (almost) from Scratch}, author={Ronan Collobert and Jason Weston and L{\'e}on Bottou and Michael Karlen and Koray Kavukcuoglu and Pavel P. Kuksa}, journal={Journal of Machine Learning Research}, year={2011}, volume={12}, pages={2493-2537} }