A Lightweight Environment for Learning Experimental IR Research Practices

Zeynep Akkalyoncu Yilmaz, C. Clarke, Jimmy J. Lin
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引用次数: 12

Abstract

Tools, computing environments, and datasets form the three critical ingredients for teaching and learning the practical aspects of experimental IR research. Assembling these ingredients can often be challenging, particularly in the context of short courses that cannot afford large startup costs. As an initial attempt to address these issues, we describe materials that we have developed for the "Introduction to IR" session at the ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (AFIRM 2020), which builds on three components: the open-source Lucene search library, cloud-based notebooks, and the MS MARCO dataset. We offer a self-reflective evaluation of our efforts and hope that our lessons shared can benefit future efforts.
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学习实验IR研究实践的轻量级环境
工具、计算环境和数据集构成了IR实验研究实践方面教学的三个关键要素。整合这些要素通常是具有挑战性的,特别是在短期课程的背景下,这些课程无法承担大量的启动成本。作为解决这些问题的初步尝试,我们描述了我们为ACM SIGIR/SIGKDD非洲暑期学校关于数据挖掘和搜索机器学习(AFIRM 2020)的“IR入门”会议开发的材料,它建立在三个组成部分:开源Lucene搜索库,基于云的笔记本电脑和MS MARCO数据集。我们对我们的努力进行了自我反思,并希望我们分享的经验教训对未来的努力有益。
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