# Integrating the Probabilistic Models BM25/BM25F into Lucene

**Authors:** Joaquín Pérez-Iglesias, José R. Pérez-Agüera, Víctor Fresno, Yuval Z. Feinstein  
**arXiv:** [0911.5046](https://arxiv.org/abs/0911.5046)  
**Date:** 2009-11-26  
**Summary page:** https://www.summarizepaper.com/en/paper/0911.5046v2/

## Key points

- The paper discusses integrating BM25 and BM25F models into Lucene Java Framework
- BM25 and BM25F are recognized for exceptional performance in Information Retrieval (IR) community
- BM25 is designed for plain text documents, while BM25F caters to structured format documents
- Integration of these models into Lucene enhances search capabilities in information retrieval systems
- Authors provide insights on effectively utilizing these models within the framework
- Practical approach for implementing state-of-the-art IR techniques is offered
- Valuable resource for leveraging BM25 and BM25F in projects
- Software related to this implementation can be easily accessed through a provided link

## Summary

The paper "Integrating the Probabilistic Models BM25/BM25F into Lucene" by Joaquín Pérez-Iglesias, José R. Pérez-Agüera, Víctor Fresno, and Yuval Z. Feinstein delves into the implementation of BM25 and BM25F models using the Lucene Java Framework. These models have gained recognition in the Information Retrieval (IR) community for their exceptional performance at TREC evaluations. Specifically designed for plain text documents without predefined fields, BM25 is complemented by BM25F which caters to structured format documents. The integration of these models into Lucene offers advanced tools for enhancing search capabilities in information retrieval systems. The authors provide insights on effectively utilizing these models within the framework and offer a practical approach for implementing state-of-the-art IR techniques. This document serves as a valuable resource for those looking to leverage BM25 and BM25F in their own projects. Additionally, readers can easily access the software related to this implementation through a provided link, facilitating further exploration of these probabilistic models within the Lucene environment. Overall, this paper contributes to advancing research in IR by demonstrating how cutting-edge techniques can seamlessly integrate into existing frameworks for improved search functionality and retrieval performance.