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  Investigating Retrieval Method Selection with Axiomatic Features

Arora, S., & Yates, A. (2019). Investigating Retrieval Method Selection with Axiomatic Features. Retrieved from http://arxiv.org/abs/1904.05737.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-02BF-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-02C0-0
Genre: Paper

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arXiv:1904.05737.pdf (Preprint), 355KB
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arXiv:1904.05737.pdf
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File downloaded from arXiv at 2019-07-10 11:02 Algorithm Selection and Meta-Learning in Information Retrieval (AMIR'19) workshop at ECIR'19
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 Creators:
Arora, Siddhant1, Author              
Yates, Andrew1, Author              
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Free keywords: Computer Science, Information Retrieval, cs.IR
 Abstract: We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combine the methods' relevance scores into an overall relevance score. Inspired by neural models' different properties with regard to IR axioms, these predictions are based on features that quantify axiom-related properties of the query and its top ranked documents. We conduct an evaluation on TREC Web Track data and find that the meta-learner often significantly improves over the individual methods. Finally, we conduct feature and query weight analyses to investigate the meta-learner's behavior.

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Language(s): eng - English
 Dates: 2019-04-112019
 Publication Status: Published online
 Pages: 14 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1904.05737
URI: http://arxiv.org/abs/1904.05737
BibTex Citekey: Arora_arXiv1904.05737
 Degree: -

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