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A quasi-current representation for information needs inspired by Two-State Vector Formalism

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  • Wang, Panpan
  • Hou, Yuexian
  • Li, Jingfei
  • Zhang, Yazhou
  • Song, Dawei
  • Li, Wenjie

Abstract

Recently, a number of quantum theory (QT)-based information retrieval (IR) models have been proposed for modeling session search task that users issue queries continuously in order to describe their evolving information needs (IN). However, the standard formalism of QT cannot provide a complete description for users’ current IN in a sense that it does not take the ‘future’ information into consideration. Therefore, to seek a more proper and complete representation for users’ IN, we construct a representation of quasi-current IN inspired by an emerging Two-State Vector Formalism (TSVF). With the enlightenment of the completeness of TSVF, a “two-state vector” derived from the ‘future’ (the current query) and the ‘history’ (the previous query) is employed to describe users’ quasi-current IN in a more complete way. Extensive experiments are conducted on the session tracks of TREC 2013 & 2014, and show that our model outperforms a series of compared IR models.

Suggested Citation

  • Wang, Panpan & Hou, Yuexian & Li, Jingfei & Zhang, Yazhou & Song, Dawei & Li, Wenjie, 2017. "A quasi-current representation for information needs inspired by Two-State Vector Formalism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 627-637.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:627-637
    DOI: 10.1016/j.physa.2017.04.145
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    References listed on IDEAS

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    1. Li, Jingfei & Zhang, Peng & Song, Dawei & Hou, Yuexian, 2016. "An adaptive contextual quantum language model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 51-67.
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