追踪挑战2011:使用TraceLab评估本地与全球IDF对追踪检索的影响

TEFSE '11 Pub Date : 2011-05-23 DOI:10.1145/1987856.1987874
Adam Czauderna, M. Gibiec, Greg Leach, Yubin Li, Yonghee Shin, E. Keenan, J. Cleland-Huang
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引用次数: 13

摘要

许多跟踪检索算法都采用标准的tf-idf(术语频率,逆文档频率)技术来对各种术语进行加权。在本文中,我们通过比较仅基于查询中的局部术语计算idf与基于美国国家语料库中记录的通用术语使用计算idf的有效性来解决大挑战C-GC1。我们还通过使用TraceLab alpha版本设计和执行实验来解决大挑战L-GC1和L-GC2。TraceLab是一个实验工作台,它允许研究人员以图形化的方式建模和执行可追溯性实验,作为组件的工作流。实验结果表明,在所有研究的情况下,局部idf方法都优于或匹配全局方法。
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Traceability challenge 2011: using TraceLab to evaluate the impact of local versus global IDF on trace retrieval
Numerous trace retrieval algorithms incorporate the standard tf-idf (term frequency, inverse document frequency) technique to weight various terms. In this paper we address Grand Challenge C-GC1 by comparing the effectiveness of computing idf based only on the local terms in the query, versus computing it based on general term usage as documented in the American National Corpus. We also address Grand Challenges L-GC1 and L-GC2 by setting ourselves the additional task of designing and conducting the experiments using the alpha-release of TraceLab. TraceLab is an experimental workbench which allows researchers to graphically model and execute a traceability experiment as a workflow of components. Results of the experiment show that the local idf approach exceeds or matches the global approach in all of the cases studied.
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