Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55524
Title: Modeling quantum entanglements in quantum language models
Authors: Xie, M
Hou, Y
Zhang, P
Li, J
Li, W 
Song, D
Issue Date: 2015
Publisher: International Joint Conferences on Artificial Intelligence
Source: 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, 25-31 July 2015, p. 1362-1368 How to cite?
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.
URI: http://hdl.handle.net/10397/55524
ISBN: 9781577357384
ISSN: 1045-0823
Appears in Collections:Conference Paper

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