  In this paper, we propose a novel approach for measuring the degree of
similarity between categories of two pieces of Persian text, which were
published as descriptions of two separate advertisements. We built an
appropriate dataset for this work using a dataset which consists of
advertisements posted on an e-commerce website. We generated a significant
number of paired texts from this dataset and assigned each pair a score from 0
to 3, which demonstrates the degree of similarity between the domains of the
pair. In this work, we represent words with word embedding vectors derived from
word2vec. Then deep neural network models are used to represent texts.
Eventually, we employ concatenation of absolute difference and bit-wise
multiplication and a fully-connected neural network to produce a probability
distribution vector for the score of the pairs. Through a supervised learning
approach, we trained our model on a GPU, and our best model achieved an F1
score of 0.9865.
