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Department of Machine Learning
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Software : senna 
SENNA is a Neural Network Architecture for Semantic Extraction.
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, achieving or exceeding state-of-the-art performance in each on four benchmark tasks. This versatility is achieved by avoiding excessive 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 excellent performance and minimal computational requirements. Please proceed to SENNA's webpage for details and download. Publications :3Related Projects :semantic_extraction. |
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