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A Study of the Overlap Among Document Representations

Published: 02 August 2017 Publication History

Abstract

Most previous investigations comparing the performance of different representations have used recall and precision as performance measures. However, there is evidence to show that these measures are insensitive to an important difference between representations. To explain, two representations may perform similarly on these measures, while retrieving very different sets of documents. Equivalence of representations should be decided on the basis of similarity in performance and similarity in the documents retrieved. This study compared the performance of four representations in the PsycAbs database. In addition, overlap between retrieved sets was also computed where overlap is the proportion of retrieved documents that are the same for pairs of document representations. Results indicate that for any two representations considered, performance values differed slightly while overlap scores were also low, thus supporting the evidence that recall and precision as performance measures mask differences between the sets of retrieved documents. Results are interpreted to propose an optimal ordering of the representations and to examine the contribution of each representation given this combination.

References

[1]
Katzer, Jeffrey, et al. A Study of the Overlap Among Document Representations. Information Technology, 1982. i, pp. 201--273.
[2]
Katzer, Jeffrey. A Study of the Impact of Representations in Information Retrieval Systems. Final report for Grant NSF-IST- 79-21468 to the National Science Foundation, July 1982.
[3]
McGill, Michael J. et al. An Evaluation of Factors Affecting Document Ranking by Information Retrieval Systems. Final Report For Grant NSF-IST-78-10454 to the National Science Foundation, October 1979.

Cited By

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  • (2024)A Blueprint of IR Evaluation Integrating Task and User CharacteristicsACM Transactions on Information Systems10.1145/367516242:6(1-38)Online publication date: 1-Jul-2024
  • (2020)Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief NetworksMolecules10.3390/molecules2601012826:1(128)Online publication date: 29-Dec-2020

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            Published In

            cover image ACM SIGIR Forum
            ACM SIGIR Forum  Volume 51, Issue 2
            SIGIR Test-of-Time Awardees 1978-2001
            July 2017
            276 pages
            ISSN:0163-5840
            DOI:10.1145/3130348
            • Editors:
            • Donna Harman,
            • Diane Kelly
            Issue’s Table of Contents
            Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            Published: 02 August 2017
            Published in SIGIR Volume 51, Issue 2

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            Cited By

            View all
            • (2024)A Blueprint of IR Evaluation Integrating Task and User CharacteristicsACM Transactions on Information Systems10.1145/367516242:6(1-38)Online publication date: 1-Jul-2024
            • (2020)Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief NetworksMolecules10.3390/molecules2601012826:1(128)Online publication date: 29-Dec-2020

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