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2019 | OriginalPaper | Chapter

BM25 Beyond Query-Document Similarity

Authors : Billel Aklouche, Ibrahim Bounhas, Yahya Slimani

Published in: String Processing and Information Retrieval

Publisher: Springer International Publishing

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Abstract

The massive growth of information produced and shared online has made retrieving relevant documents a difficult task. Query Expansion (QE) based on term co-occurrence statistics has been widely applied in an attempt to improve retrieval effectiveness. However, selecting good expansion terms using co-occurrence graphs is challenging. In this paper, we present an adapted version of the BM25 model, which allows measuring the similarity between terms. First, a context window-based approach is applied over the entire corpus in order to construct the term co-occurrence graph. Afterward, using the proposed adapted version of BM25, candidate expansion terms are selected according to their similarity with the whole query. This measure stands out by its ability to evaluate the discriminative power of terms and select semantically related terms to the query. Experiments on two ad-hoc TREC collections (the standard Robust04 collection and the new TREC Washington Post collection) show that our proposal outperforms the baselines over three state-of-the-art IR models and leads to significant improvements in retrieval effectiveness.

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previous chapter Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search

next chapter Network-Based Pooling for Topic Modeling on Microblog Content

https://trec.nist.gov/data/cd45/.

https://trec.nist.gov/data/wapost/.

http://terrier.org/.

We used the CBOW implementation of word2vec and we set the vectors dimension to 300.

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Title: BM25 Beyond Query-Document Similarity
Authors: Billel Aklouche
Ibrahim Bounhas
Yahya Slimani
Publisher: Springer International Publishing
Book: String Processing and Information Retrieval
Print ISBN: 978-3-030-32685-2

Electronic ISBN: 978-3-030-32686-9

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-32686-9_5

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