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In this paper, we propose a novel approach for measuring the degree ofsimilarity between categories of two pieces of Persian text, which werepublished as descriptions of two separate advertisements. We built anappropriate dataset for this work using a dataset which consists ofadvertisements posted on an e-commerce website. We generated a significantnumber of paired texts from this dataset and assigned each pair a score from 0to 3, which demonstrates the degree of similarity between the domains of thepair. In this work, we represent words with word embedding vectors derived fromword2vec. Then deep neural network models are used to represent texts.Eventually, we employ concatenation of absolute difference and bit-wisemultiplication and a fully-connected neural network to produce a probabilitydistribution vector for the score of the pairs. Through a supervised learningapproach, we trained our model on a GPU, and our best model achieved an F1score of 0.9865.

A Deep Learning-Based Approach for Measuring the Domain Similarity of Persian Texts