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Natural Language Processing (Almost) from Scratch

Journal of Machine Learning Research 12 (2011) 2493-2537 Submitted 1/10; Revised 11/10; Published 8/11. Natural Language Processing (Almost) from Scratch Ronan Collobert RONAN @ COLLOBERT. COM. Jason Weston JWESTON @ GOOGLE . COM. Le on Bottou LEON @ BOTTOU . ORG. Michael Karlen MICHAEL . KARLEN @ GMAIL . COM. Koray Kavukcuoglu KORAY @ CS . NYU . EDU. Pavel Kuksa PKUKSA @ CS . RUTGERS . EDU. NEC Laboratories America 4 Independence Way Princeton, NJ 08540. Editor: Michael Collins Abstract We propose a unified neural network architecture and learning algorithm that can be applied to var- ious 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.

We propose a unified neural network architecture and learnin g algorithm that can be applied to var- ... basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for ... They perform dynamic programming at test time. Later, they improved their results up to 93.91% (Kudo and Matsumoto, 2001) using an

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