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