%0 Generic %1 le2015compositional %A Le, Phong %A Zuidema, Willem %D 2015 %K lstm ma-zehe neuralnets rnn sentimentanalysis %T Compositional Distributional Semantics with Long Short Term Memory %U http://arxiv.org/abs/1503.02510 %X 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.

@misc{le2015compositional, 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.}, added-at = {2017-01-13T14:11:42.000+0100}, author = {Le, Phong and Zuidema, Willem}, biburl = {https://www.bibsonomy.org/bibtex/27cd995fcd0c9d70513c85e9a680412a1/albinzehe}, description = {Compositional Distributional Semantics with Long Short Term Memory}, interhash = {673a113cfe425f8eac7cf07e8a425cba}, intrahash = {7cd995fcd0c9d70513c85e9a680412a1}, keywords = {lstm ma-zehe neuralnets rnn sentimentanalysis}, note = {cite arxiv:1503.02510Comment: 10 pages, 7 figures}, timestamp = {2017-01-13T14:11:42.000+0100}, title = {Compositional Distributional Semantics with Long Short Term Memory}, url = {http://arxiv.org/abs/1503.02510}, year = 2015 }