Determining the Composition Functions of Persian Non-standard Sentences in Terminology using a Deep Learning Fuzzy Neural Network Model

Abstract

Organizations can enhance the speed of well-informed decision-making by correctly understanding and using data. Since there is a tremendous gap between the speed of data processing and data generation in the world, exploring data mining in the digital world becomes inevitable. In the Persian language, similar to other languages, with the expansion of communications through social networks, the spelling of words has become abridged and the engagement of foreign loan words and emoticons has been increasing on a daily basis. Given the richness of Persian and its typographical-grammatical similarities to Arabic, research in Persian can be applied to other akin languages as well. In this regard, the current study deals with data mining of Persian non-standard sentences in order to find the function of each word in the sentence. The volume of computation might be limited in traditional methods of natural language processing for each factor contributing to functions. That is because the minimum number of computations is (5 × number of words 9) + (5 × number of words 15). Therefore, this study adopted the Gated Recurrent Unit (GRU) method to process such computations. The newly proposed method reinforces the results of word function identification by using two categories of independent and dependent Persian language functions as well as five factors contributing to the functions of words in sentences as five output gates. Meanwhile, the values of the training tables in this method are fuzzy, where the center-of-gravity method is adopted to decide on the values as well as to reduce the complexity and ambiguity of such computations on the probability of each event occurring. Therefore, the new method is briefly called fuzzy GRU. The results show that the proposed algorithm achieves 80 % reduction in the amount of calculations per gate of updates and reinforcement is approximately 2 % up from 67 % in standard sentences to 69 % of the non-standard sentences.