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• W2117130368 abstract "We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance." @default.
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• W2117130368 date "2008-01-01" @default.
• W2117130368 modified "2023-10-14" @default.
• W2117130368 title "A unified architecture for natural language processing" @default.
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• W2117130368 doi "https://doi.org/10.1145/1390156.1390177" @default.
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