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International Conference on Machine Learning, Image Processing, Network Security and Data Sciences

MIND 2020: Machine Learning, Image Processing, Network Security and Data Sciences pp 1–16Cite as

Placing Query Term Proximity in Search Context

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1240))

Abstract

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e. term frequency (tf), inverse document frequency (idf), etc.. Query Term Proximity (QTP) within matched documents is mostly under-explored for the relevance estimation in the information retrieval. In this paper, we systematically review the lineage of the notion of QTP in IR and proposed a novel framework for relevance estimation. The proposed framework is referred as Adaptive QTP based User Information Retrieval (AQtpUIR), is intended to promote the document’s relevance among all relevant retrieved ones. Here, the relevance estimation is a weighted combination of document-term (DT) statistics and query-term (QT) statistics. The notions ‘term-term query proximity’ is a simple aggregation of contextual aspects of user search in relevance estimates and query formation. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The design analysis asserts the usability of QTP measures to balance several seeking tradeoffs, e.g. relevance, novelty, result diversity (Coverage and Topicality), and highlight various inherent challenges and issue of the proposed work.

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Authors and Affiliations

  1. National Institute of Technology, Kurukshetra, Kurukshetra, 136119, Haryana, India

    Tirthankar Barik & Vikram Singh

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  1. Tirthankar Barik
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  2. Vikram Singh
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Correspondence to Vikram Singh .

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Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Arup Bhattacharjee

  2. National Institute Of Technology Silchar, Silchar, India

    Samir Kr. Borgohain

  3. National Institute of Technology Silchar, Silchar, India

    Badal Soni

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Gyanendra Verma

  5. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Barik, T., Singh, V. (2020). Placing Query Term Proximity in Search Context. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_1

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  • DOI: https://doi.org/10.1007/978-981-15-6315-7_1

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  • Print ISBN: 978-981-15-6314-0

  • Online ISBN: 978-981-15-6315-7

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