Scholarlink

Dependency parsing is essential in Natural Language Processing (NLP), but parser performance varies across languages and domains, especially in low-resource settings. While aggregation methods have improved other NLP tasks, their role in dependency parsing remains largely unexplored. This study evaluates three unsupervised aggregation frameworks: Maximum Spanning Tree (MST), Conflict Resolution on Heterogeneous Data (CRH), and a Customized Ising Model (CIM), using 71 Universal Dependency test treebanks covering 49 languages. Results show that the CIM consistently outperforms individual parsers and other aggregation approaches by effectively estimating parser quality. These findings highlight the potential of parse tree aggregation for improving parsing robustness in multilingual and low-resource settings.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beyond Single Parsers: An Empirical Analysis of Dependency Parse Tree Aggregation

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Adithya Kulkarni,
  • Mohna Chakraborty,
  • Oliver Eulenstein,
  • Qi Li

摘要

Dependency parsing is essential in Natural Language Processing (NLP), but parser performance varies across languages and domains, especially in low-resource settings. While aggregation methods have improved other NLP tasks, their role in dependency parsing remains largely unexplored. This study evaluates three unsupervised aggregation frameworks: Maximum Spanning Tree (MST), Conflict Resolution on Heterogeneous Data (CRH), and a Customized Ising Model (CIM), using 71 Universal Dependency test treebanks covering 49 languages. Results show that the CIM consistently outperforms individual parsers and other aggregation approaches by effectively estimating parser quality. These findings highlight the potential of parse tree aggregation for improving parsing robustness in multilingual and low-resource settings.