Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks. Parsers' quality often varies depending on the domain and the language involved. Therefore, it is essential to combat the issue of varying quality to achieve stable performance. In various NLP tasks, aggregation methods are used for post-processing aggregation and have been shown to combat the issue of varying quality. However, aggregation methods for post-processing aggregation have not been sufficiently studied in dependency parsing tasks. In an extensive empirical study, we compare different unsupervised post-processing aggregation methods to identify the most suitable dependency tree structure aggregation method.

翻译：依存句法分析是自然语言处理（NLP）中的一项基础任务，其解析质量对众多下游任务至关重要。不同领域及语言的依存解析器质量往往存在差异。因此，为解决质量差异问题以实现稳定性能，选取合适的应对策略十分关键。在多种NLP任务中，聚合方法被用于后处理聚合，并已被证明能有效应对质量差异问题。然而，后处理聚合方法在依存句法分析任务中尚未得到充分研究。我们通过大规模实证研究，比较了多种无监督后处理聚合方法，以筛选出最适用于依存句法树结构的聚合方法。