Non-separability Effects in Cognitive

Semantic Retrieving

Aleksey V. Platonov, Igor A. Bessmertny, Evgeny K. Semenenko, and Alexander P. Alodjants

Abstract This paper discusses a Bell test analogue known in quantum physics, which allows determining the presence of non-separability features by using semantic search of information and document ranking for articles in Russian. The model of Bell test in semantics is based on hyperspace analogue to language (HAL) algorithm provides to obtain vector representation of words (in Hilbert space) using the dictionary index and considering the word order. We show the existence of certain quantum-like correlations between two words of the userÕs query; these correlations cannot be taken into account in the classical probabilistic description. We predict that the contextuality revealed can be regarded as human cognitive level both while writing of certain texts and queries to them.

Keywords Information retrieval systems á Decision-making á Quantum cognitive science á Quantum entanglement á Contextuality á Machine learning

1 Introduction

Over the past decade, the rapid growth of information resources in terms of information transmission and processing has led to an exponential growth of data, most of which are poorly structured or have no structure at all. The need to proceed and analyze such data in real time is a serious problem, which is directly related to the safety of society itself in various areas of economics, Þnances, and social sphere. Thematic modeling as one of the machine learning paradigms is an important tool for modern text and document analysis and has a direct application to the problems of information retrieval. On the other hand, quantum approach for information retrieving allows to take into account some peculiarities (disturbances) occurring during the ÒinteractionÓ of the user and ÒsmartÓ search system similarly to

A. V. Platonov á I. A. Bessmertny á E. K. Semenenko á A. P. Alodjants ( ) ITMO University, Saint Petersburg, Russia

D. Aerts et al. (eds.), Quantum-Like Models for Information Retrieval and Decision-Making, STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health, https://doi.org/10.1007/978-3-030-25913-6_2