Thanks for pointing this out. The same acronym is used to refer to both versions. I like the description of Recursive Neural Nets versus Recurrent Neural Nets in this Stack Overflow post [1].
> A recursive network is just a generalization of a recurrent network. In a recurrent network the weights are shared (and dimensionality remains constant) along the length of the sequence because how would you deal with position-dependent weights when you encounter a sequence at test-time of different length to any you saw at train-time. In a recursive network the weights are shared (and dimensionality remains constant) at every node for the same reason.
In the TreeRNN (Tree Recursive Neural Nets) Github project and associated paper [2], there is indeed an implementation of a TreeLSTM which is a recursive LSTM inspired by the recurrent version.
> A recursive network is just a generalization of a recurrent network. In a recurrent network the weights are shared (and dimensionality remains constant) along the length of the sequence because how would you deal with position-dependent weights when you encounter a sequence at test-time of different length to any you saw at train-time. In a recursive network the weights are shared (and dimensionality remains constant) at every node for the same reason.
In the TreeRNN (Tree Recursive Neural Nets) Github project and associated paper [2], there is indeed an implementation of a TreeLSTM which is a recursive LSTM inspired by the recurrent version.
[1]: http://stats.stackexchange.com/a/158995/92040
[2]: http://www.aclweb.org/anthology/P15-1150