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.


Published in:
Journal of Machine Learning Research, 12, 2493−2537
Year:
2011
Laboratories:




 Record created 2013-12-19, last modified 2018-03-17

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