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中的一项基础任务，其质量对许多下游任务至关重要。由于不同领域和语言所涉及的依存句法分析器质量往往存在差异，因此解决质量参差不齐的问题以实现稳定性能显得尤为关键。在各种自然语言处理任务中，聚合方法被用于后处理聚合，并已被证明能够应对质量差异问题。然而，在后处理聚合中，聚合方法在依存句法分析任务中的研究尚不充分。通过一项广泛的实证研究，我们比较了不同的无监督后处理聚合方法，以确定最合适的依存树结构聚合方法。