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Analysis of Relevant Text Fragments for Different Search Task Types

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Book cover Information Retrieval Technology (AIRS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11292))

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Abstract

This paper investigates the trend of relevant text fragments by task type. The search results of fine-grained information retrieval systems propose not documents but text fragments. We hypothesize that the properties of relevant text fragments depend on the task type. To reveal these properties, we evaluate a relevant text fragment to judge (1) its granularity (e.g., word, phrase, or sentence) and (2) its structural complexity. Our analysis shows that a task type based on more complex information needs has a larger granularity of relevant text fragments. On the other hand, the complexity of task type’s information needs does not necessarily correlate with the structural complexity of the relevant text fragments.

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Notes

  1. 1.

    https://trec.nist.gov/.

  2. 2.

    http://www.mobileclick.org/.

  3. 3.

    Some previous studies such as [10, 11, 13] focus on automatic task type classification. However, this study just uses the task type-tagged queries.

  4. 4.

    http://www.lsc.cs.titech.ac.jp/keyaki/dataSet/mc2_iunitGranular_dist.tsv.

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Acknowledgments

This work was partly supported by ACT-I, JST.

Author information

Authors and Affiliations

  1. Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro-ku, Tokyo, Japan

    Atsushi Keyaki & Jun Miyazaki

Authors
  1. Atsushi Keyaki
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  2. Jun Miyazaki
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Corresponding author

Correspondence to Atsushi Keyaki .

Editor information

Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Yuen-Hsien Tseng

  2. Waseda University, Tokyo, Japan

    Tetsuya Sakai

  3. School of Information Systems, Singapore Management University, Singapore, Singapore

    Jing Jiang

  4. Academia Sinica, Taipei, Taiwan

    Lun-Wei Ku

  5. Huawei Research America, Champaign, IL, USA

    Dae Hoon Park

  6. National Chiayi University, Chiayi City, Taiwan

    Jui-Feng Yeh

  7. Yuan Ze University, Taoyuan City, Taiwan

    Liang-Chih Yu

  8. National Central University, Taoyuan City, Taiwan

    Lung-Hao Lee

  9. National Taiwan Normal University, Taipei City, Taiwan

    Zhi-Hong Chen

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Cite this paper

Keyaki, A., Miyazaki, J. (2018). Analysis of Relevant Text Fragments for Different Search Task Types. In: Tseng, YH., et al. Information Retrieval Technology. AIRS 2018. Lecture Notes in Computer Science(), vol 11292. Springer, Cham. https://doi.org/10.1007/978-3-030-03520-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-03520-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03519-8

  • Online ISBN: 978-3-030-03520-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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