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Modeling Quantum Entanglements in Quantum Language Models

Xie, Mengjiao; Hou, YueXian; Zhang, Peng; Li, Jingfei; Li, Wenjie and Song, Dawei (2015). Modeling Quantum Entanglements in Quantum Language Models. In: 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), 25-31 Jul 2015, Buenos Aires, Argentina, AAAI Press, pp. 1362–1368.

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Abstract

Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR). Nevertheless, QLM's dependency is based on co-occurrences of terms and has not yet taken into account the Quantum Entanglement (QE), which is a key quantum concept and has a significant cognitive implication. In QT, an entangled state can provide a more complete description for the nature of realities, and determine intrinsic correlations of considered objects globally, rather than those co-occurrences on the surface. It is, however, a real challenge to decide and measure QE using the classical statistics of texts in a post-measurement configuration. In order to circumvent this problem, we theoretically prove the connection between QE and statistically Unconditional Pure Dependence (UPD). Since UPD has an implementable deciding algorithm, we can in turn characterize QE by extracting the UPD patterns from texts. This leads to a measurable QE, based on which we further advance the existing QLM framework. We empirically compare our model with related models, and the results demonstrate the effectiveness of our model.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 Association for the Advancement of Artificial Intelligence
Project Funding Details:
Funded Project NameProject IDFunding Body
Not Set2015AA015403Chinese 863 Program
Not Set2013CB329304Chinese 973 Program
Not Set2014CB744604Chinese 973 Program
Not Set61272291Chinese NSF Project
Not Set61402324Chinese NSF Project
Not Set61272265Chinese NSF Project
Not Set15JCZDJC31100Key NSF Project of Chinese Tianjin
Not Set14ZDB153Major Project of Chinese National Social Science Fund
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Related URLs:
Item ID: 44131
Depositing User: Dawei Song
Date Deposited: 24 Aug 2015 09:12
Last Modified: 02 May 2018 14:12
URI: http://oro.open.ac.uk/id/eprint/44131
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