Long Short-Term Memory Over Tree Structures
Table 2: Performances of models trained with only root labels (the first two rows) and models that use both root and leaf labels (the last two rows).
['Models', 'roots']
More real-life settings We further compare S-LSTM with RNTN in two more experimental settings. ROOT LBLS besides the model names stands for root labels; that is, only the gold labels of the sentence level are used to train the model. Also, annotating all phrases is expensive. However, these should not be regarded as comments on the value of the Sentiment Tree Bank. (LEAFLBLS along the side of the model names stands for leaf labels), and so there is no out-of-vocabulary concern. Using real sentiment lexicons is expected to have a performance between the two settings here.