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- W2960930698 abstract "We are proposing an extension of the recursive neural network that makes use of a variant of the long short-term memory architecture. The extension allows information low in parse trees to be stored in a memory register (the `memory cell') and used much later higher up in the parse tree. This provides a solution to the vanishing gradient problem and allows the network to capture long range dependencies. Experimental results show that our composition outperformed the traditional neural-network composition on the Stanford Sentiment Treebank." @default.
- W2960930698 created "2019-07-23" @default.
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- W2960930698 date "2015-01-01" @default.
- W2960930698 modified "2023-09-23" @default.
- W2960930698 title "Compositional Distributional Semantics with Long Short Term Memory" @default.
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- W2960930698 doi "https://doi.org/10.18653/v1/s15-1002" @default.
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