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. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

Authors

• Ronan Collobert IDIAP
• Jason Weston
• Léon Bottou
• Michael Karlen
• Koray Kavukcuoglu
• Pavel Kuksa

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