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BM25
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BM25

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2023 Sep 19 15:28
Editor
Editor
Seonglae ChoSeonglae Cho
Edited
Edited
2025 Jan 29 13:2
Refs
Refs
Sparse vector
Binary independence model

TF-IDF
+ passage length

Two Poisson model
검색엔진, 추천 시스템 등에서 아직까지도 많이 사용되는 알고리즘
 
 

BM25+
Relevance Feedback
based on
Contingency Table

 
 
 
Okapi BM25
In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others.
Okapi BM25
https://en.wikipedia.org/wiki/Okapi_BM25
(4강) Passage Retrieval - Sparse Embedding
강의소개4강에서는 단어기반 문서 검색에 대해 배워보겠습니다. 먼저 문서 검색 (Passage retrieval)이란 어떤 문제인지에 대해 알아본 후, 문서 검색을 하는 방법에 대해 알아보겠습니다. 문서 검색을 하기 위해서는 문서를 embedding의 형태로 변환해 줘야
(4강) Passage Retrieval - Sparse Embedding
https://velog.io/@changyong93/4강-Passage-Retrieval-Sparse-Embedding#bm25란
(4강) Passage Retrieval - Sparse Embedding
 
 

Backlinks

Retrieval ModelOpen Generative QA

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BM25
Copyright Seonglae Cho