Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling. However, these approaches do not support more complex graph-based representations, such as semantic dependencies or enhanced universal dependencies, as they cannot handle reentrancy or cycles. By extending them, we define a range of unbounded and bounded linearizations that can be used to cast graph parsing as a tagging task, enlarging the toolbox of problems that can be solved under this paradigm. Experimental results on semantic dependency and enhanced UD parsing show that with a good choice of encoding, sequence-labeling dependency graph parsers combine high efficiency with accuracies close to the state of the art, in spite of their simplicity.

翻译：已有多种线性化方法被提出，将句法依存解析转化为序列标注任务。然而，这些方法无法处理更复杂的基于图的表示形式（如语义依存或增强型通用依存），因为它们不能处理重入或循环结构。通过扩展这些方法，我们定义了一系列无界和有界的线性化方案，可用于将图解析转化为标注任务，从而扩展了该范式下可解决的问题范围。在语义依存和增强型通用依存解析上的实验结果表明，通过选择合适的编码方式，序列标注式依存图解析器在保持高计算效率的同时，其准确率可接近当前最优水平，尽管其结构相对简单。