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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.
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https://dl.acm.org/doi/10.5555/1953048.2078186
https://dl.acm.org/doi/pdf/10.5555/1953048.2078186
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