将复杂依赖图解析转化为序列标注任务，提升效率与精度。
Dependency Graph Parsing as Sequence Labeling
- 提出无界和有界线性化方法，支持含重叠与环路的图结构。
- 在语义依存和增强通用依存数据集上达到接近顶尖水平的准确率。
- 适合追求高效且简洁解析方案的研究者或工程应用。
现有方法将句法依存解析转化为序列标注，但难以处理语义依存或增强通用依存等复杂图结构，因其无法处理重叠或环路。本文通过扩展这些方法，提出一系列无界与有界线性化策略，使图解析可转化为标注任务，拓展了该范式适用范围。在语义依存和增强通用依存数据集上的实验表明，采用合适编码后，序列标注型图解析器兼具高效率与接近当前最优的准确率，尽管模型结构简单。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。