Auto-tagging of Short Conversational Sentences using Transformer Methods
| Autor: | Umut Özdil, Semih Gulum, Sukru Ozan, M. Fatih Akca, Secilay Kutal, D. Emre Tasar, Oguzhan Olmez, Ceren Belhan |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language business.industry Computer science Turkish Subject (documents) AC power computer.software_genre language.human_language Machine Learning (cs.LG) Unified Modeling Language language Bit error rate Data set (IBM mainframe) Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing Transformer (machine learning model) computer.programming_language |
| DOI: | 10.48550/arxiv.2106.01735 |
| Popis: | 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. Comment: in Turkish language |
| Databáze: | OpenAIRE |
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