BM25 Pseudo Relevance Feedback using Anserini at Waseda university

Zhaohao Zeng, Tetsuya Sakai

Research output: Contribution to journalConference articlepeer-review


We built a Docker image for BM25PRF (BM25 with Pseudo Relevance Feedback) retrieval model with Anserini. Also, grid search is provided in the Docker image for parameter tuning. Experimental results suggest that BM25PRF with default parameters outperforms vanilla BM25 on robust04, but tuning parameters on 49 topics of robust04 did not further improve its effectiveness.

Original languageEnglish
Pages (from-to)62-63
Number of pages2
JournalCEUR Workshop Proceedings
Publication statusPublished - 2019 Jan 1
Event2019 Open-Source IR Replicability Challenge, OSIRRC 2019 - Paris, France
Duration: 2019 Jul 25 → …

ASJC Scopus subject areas

  • Computer Science(all)


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