1 introduction
Most models for distributed representations of phrases and sentences—that is, models where realvalued vectors are used to represent meaning—fall into one of three classes:
bag-of-words models-句子中的单词的序列关系看不出来
sequence models
tree-structured models.-包含了句法语义
这篇文章介绍了将标准lstm改进为树结构一般化过程，在序列lstm上可以表示出句子的含义a generalization of
区别：
the standard LSTM composes -- hidden state from the input at the current time step and the hidden state of the LSTM unit in the previous time step,
the tree-structured LSTM, orTree-LSTM--composes its state from an input vector and the hidden states of arbitrarily many child units.
标准lstm是tree-lstm的一个特例，看做tree-lstm的每个内部节点只有一个孩子
斯坦福的sentiment treebank：
treebank的形式如下
(0 (1 You) (2 (3 can) (4 (5 (6 run) (7 (8 this) (9 code))) (10 (11 with) (12 (13 (14 our) (15 (16 trained) (17 model))) (18 (19 on) (20 (21 (22 text) (23 files)) (24 (25 with) (26 (27 the) (28 (29 following) (30 command)))))))))))
这是句子“You can run this code with our trained model on text files with the following command”经过stanford模型计算后得到的情感treebank形式。
每个括号中的第一个元素为规则的头，比如对于左右两边都只有一个节点的规则：
(1 You)： 1->You , 1表示的是NON-Terminal字符，You表示terminal字符，和标准的pennetreebank的区别是1代表的是这个节点的情感强度，分五个等级。
(0 (1 You) (2 (3 can)…) ：
在这个规则里，右边有两个节点，是一个标准的二叉树，0-> 1, 2。