Duluth在SemEval-2025任务7中的表现：面向多语言事实核查声明检索的向量维度优化TF-IDF方法
Duluth at SemEval-2025 Task 7: TF-IDF with Optimized Vector Dimensions for Multilingual Fact-Checked Claim Retrieval
AI总结：
本研究针对SemEval-2025任务7的多语言事实核查声明检索任务，提出优化向量维度与分词策略的TF-IDF检索系统，验证了传统方法在有限资源场景下的竞争力。
摘要：
本文介绍了Duluth团队参与SemEval-2025任务7（多语言与跨语言事实核查声明检索）的方法。我们实现了一个基于TF-IDF的检索系统，并针对向量维度和分词策略开展了实验。我们的最优配置采用词级分词，词汇表规模为15000个特征，在十种语言的开发集上取得了0.78的平均success@10得分，在测试集上为0.69。我们的系统在高资源语言上表现更强，但仍显著落后于排名第一的系统，后者的平均success@10达到0.96。我们的研究结果表明，尽管先进的神经架构在多语言检索任务中日益占据主导地位，但经过适当优化的传统方法（如TF-IDF）仍是具有竞争力的基线，尤其在计算资源有限的场景下。
英文摘要：
This paper presents the Duluth approach to the SemEval-2025 Task 7 on Multilingual and Crosslingual Fact-Checked Claim Retrieval. We implemented a TF-IDF-based retrieval system with experimentation on vector dimensions and tokenization strategies. Our best-performing configuration used word-level tokenization with a vocabulary size of 15,000 features, achieving an average success@10 score of 0.78 on the development set and 0.69 on the test set across ten languages. Our system showed stronger performance on higher-resource languages but still lagged significantly behind the top-ranked system, which achieved 0.96 average success@10. Our findings suggest that though advanced neural architectures are increasingly dominant in multilingual retrieval tasks, properly optimized traditional methods like TF-IDF remain competitive baselines, especially in limited compute resource scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
