Classification of short texts using a wave model

Quantum computing algorithms are actively developed and applied in the field of natural language processing. The authors of the paper proposed a new quantum-like method for classifying short texts. The basis of the method is the representation of the text as an ensemble of elementary particles. The value of the detection probability amplitude of a given ensemble at the selected points in space is chosen as a classification criterion. In this case, the space is understood as a vector space described using the distributive-semantic model of the language. The authors suggested one of the possible ways of interpreting the parameters of the wave function that describes the behavior of an elementary particle, as well as an algorithm for calculating the probability amplitude taking into account these parameters. For the experimental research of the described method, authors performed the classification of Internet communities by topics. For the analysis, the names and the “information” section of communities were used. In total, 100 groups of the social network “VKontakte” belonging to five various topics were taken. The proposed model showed rather high classification accuracy (91 % in general on the data set and from 75 % to 95 % within individual classes). The proposed model is supposed to be used to classify user comments about goods, services and events, as well as to determine some properties of the psychological portraits of users of online communities.