A Span-based Linearization for Constituent Trees
Table 1: Comparison on different phrases types. Here we only list top nine types.
['Type', 'NP', 'VP', 'S', 'PP', 'SBAR', 'ADVP', 'ADJP', 'QP', 'WHNP']
S4SS3SSS0Px2 Influence of Constituent Type Then we compare the accuracy of different constituent types. Our model all performs better than PSN model, especially in types SBAR, ADJP and QP. When optimizing the representation of one split point, our model can consider all of the words before it, which can be helpful to predict some types. For example, when we predict an adjective phrase (ADJP), its representation has fused the words’ information before it (e.g. linking verb like “is”), which can narrow the scope of prediction.