论文标题
基于跨度的成分树的线性化
A Span-based Linearization for Constituent Trees
论文作者
论文摘要
我们提出了组成树的新型线性化，以及一个新的局部归一化模型。对于句子中的每个拆分点，我们的模型在所有跨度以该拆分点结束的跨度上计算归一化器，然后预测它们的树跨度。与全球模型相比，我们的模型是快速且可行的。与以前的本地模型不同，我们的线性化方法直接与跨度有关，并在执行跨度预测时考虑了更多的本地特征，这更容易解释和有效。 PTB（95.8 F1）和CTB（92.4 F1）的实验表明，我们的模型显着胜过现有的本地模型，并有效地通过全球模型实现了竞争成果。
We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.
