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The problem of categorizing short speech sentences according to theirsemantic features with high accuracy is a subject studied in natural languageprocessing. In this study, a data set created with samples classified in 46different categories was used. Examples consist of sentences taken from chatconversations between a company's customer representatives and the company'swebsite visitors. The primary purpose is to automatically tag questions andrequests from visitors in the most accurate way for 46 predetermined categoriesfor use in a chat application to generate meaningful answers to the questionsasked by the website visitors. For this, different BERT models and one GPT-2model, pre-trained in Turkish, were preferred. The classification performancesof the relevant models were analyzed in detail and reported accordingly.

Auto-tagging of Short Conversational Sentences using Transformer Methods