| Title | Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ozan, Şükrü, Taşar, D. Emre |
| Conference Name | 2021 29th Signal Processing and Communications Applications Conference (SIU) |
| Date Published | jun |
| Keywords | auto-tagging, BERT, Bit error rate, chatbot, Doc2Vec, Human Behavior, Internet, LSTM, natural language processing, pubcrawl, resilience, Resiliency, Scalability, Signal processing, social networking (online), software development management, Training data |
| Abstract | In this study, we aim to find a method to autotag sentences specific to a domain. Our training data comprises short conversational sentences extracted from chat conversations between company's customer representatives and web site visitors. We manually tagged approximately 14 thousand visitor inputs into ten basic categories, which will later be used in a transformer-based language model with attention mechanisms for the ultimate goal of developing a chatbot application that can produce meaningful dialogue.We considered three different stateof- the-art models and reported their auto-tagging capabilities. We achieved the best performance with the bidirectional encoder representation from transformers (BERT) model. Implementation of the models used in these experiments can be cloned from our GitHub repository and tested for similar auto-tagging problems without much effort. |
| DOI | 10.1109/SIU53274.2021.9477994 |
| Citation Key | ozan_auto-tagging_2021 |
Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods