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Natural Language Processing (Almost) from Scratch.

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Titel: Natural Language Processing (Almost) from Scratch.
Autoren: Collobert, Ronan1 RONAN@COLLOBERT.COM, Weston, Jason1 JWESTON@GOOGLE.COM, Bottou, Léon1 LEON@BOTTOU.ORG, Karlen, Michael1 MICHAEL.KARLEN@GMAIL.COM, Kavukcuoglu, Koray1 KORAY@CS.NYU.EDU, Kuksa, Pavel1 PKUKSA@CS.RUTGERS.EDU
Quelle: Journal of Machine Learning Research. Jul2011, Vol. 12 Issue 7, p2493-2537. 45p.
Schlagwörter: *COMPUTER network architectures, *ARTIFICIAL neural networks, *NATURAL language processing, *ALGORITHMS, *PERFORMANCE evaluation, SCRATCH (Computer program language), MACHINE learning
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:15324435