We propose a transition-based bubble parser to perform coordination structure
identification and dependency-based syntactic analysis simultaneously. Bubble
representations were proposed in the formal linguistics literature decades ago;
they enhance dependency trees by encoding coordination boundaries and internal
relationships within coordination structures explicitly. In this paper, we
introduce a transition system and neural models for parsing these
bubble-enhanced structures. Experimental results on the English Penn Treebank
and the English GENIA corpus show that our parsers beat previous
state-of-the-art approaches on the task of coordination structure prediction,
especially for the subset of sentences with complex coordination structures.

本文介绍了一种过渡式气泡分析器以同时执行协调结构识别和基于依赖的句法分析。基于气泡的结构可以通过编码协调边界和协调结构内部关系来增强依赖树，我们引入了一种过渡系统和神经模型用于解析这些增强型结构。在对英语 Penn Treebank 和英语 GENIA 语料库的实验结果表明，我们的解析器在协调结构预测任务上击败了以前的最先进方法，尤其是在具有复杂协调结构的子句中的表现更佳。