{"title":"学习实验IR研究实践的轻量级环境","authors":"Zeynep Akkalyoncu Yilmaz, C. Clarke, Jimmy J. Lin","doi":"10.1145/3397271.3401395","DOIUrl":null,"url":null,"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.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A Lightweight Environment for Learning Experimental IR Research Practices\",\"authors\":\"Zeynep Akkalyoncu Yilmaz, C. Clarke, Jimmy J. Lin\",\"doi\":\"10.1145/3397271.3401395\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":252050,\"journal\":{\"name\":\"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3397271.3401395\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397271.3401395","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Lightweight Environment for Learning Experimental IR Research Practices
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.