Which BM25 Do You Mean? A Large-Scale Reproducibility Study of Scoring Variants
暂无分享,去创建一个
Jimmy J. Lin | Leonid Boytsov | Jimmy Lin | Arjen P. de Vries | Chris Kamphuis | A. D. Vries | Leonid Boytsov | Chris Kamphuis
[1] ChengXiang Zhai,et al. When documents are very long, BM25 fails! , 2011, SIGIR.
[2] Jimmy J. Lin,et al. Old dogs are great at new tricks: column stores for ir prototyping , 2014, SIGIR.
[3] Jimmy J. Lin,et al. Overview of the 2019 Open-Source IR Replicability Challenge (OSIRRC 2019) , 2019, OSIRRC@SIGIR.
[4] Michalis Vazirgiannis,et al. Composition of TF normalizations: new insights on scoring functions for ad hoc IR , 2013, SIGIR.
[5] Jimmy J. Lin,et al. Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures , 2013, SIGIR.
[6] Jimmy J. Lin,et al. The SIGIR 2019 Open-Source IR Replicability Challenge (OSIRRC 2019) , 2019, SIGIR.
[7] ChengXiang Zhai,et al. Adaptive term frequency normalization for BM25 , 2011, CIKM '11.
[8] Stephen E. Robertson,et al. Okapi at TREC-3 , 1994, TREC.
[9] Stephen E. Robertson,et al. GatfordCentre for Interactive Systems ResearchDepartment of Information , 1996 .
[10] Andrew Trotman,et al. Improvements to BM25 and Language Models Examined , 2014, ADCS.
[11] ChengXiang Zhai,et al. Lower-bounding term frequency normalization , 2011, CIKM '11.
[12] Hugo Zaragoza,et al. The Probabilistic Relevance Framework: BM25 and Beyond , 2009, Found. Trends Inf. Retr..
[13] Andrew Trotman,et al. Towards an Efficient and Effective Search Engine , 2012, OSIR@SIGIR.
[14] ZaragozaHugo,et al. The Probabilistic Relevance Framework , 2009 .