AUTOMATED HELP DESK SYSTEM BASED ON DEEP LEARNING
Abstract
A help desk is an organization's point of contact that provides a centralized information and support management service to its employees or customers. For the efficiency of the organization, it is of great importance that the queries coming to the help desk are grouped into the correct categories and directed to the right people on time. Therefore, in this study, an automatic help desk system based on deep learning is proposed. The proposed system automatically categorizes queries according to the sentences in their titles. Word embedding method was used for this process. After the text preprocessing steps, learning is performed in three layers (embedding, flatten, and dense) and the category to which the help desk queries belong is determined. For this purpose, IT help desk queries belonging to a corporate company were used. The dataset, consisting of a total of 28.104 requests in nine different categories, is divided into 60% training, 20% validation, and 20% test set. As a result of the experiments, the classification accuracy reaching 98% revealed that the proposed model is a good candidate for an automated help desk system.
Keywords
Deep Learning , Natural Language Processing , Text Classification , Help Desk
Kaynakça
- ALRashdi, R., & O'Keefe, S. (2019). Deep learning and word embeddings for tweet classification for crisis response. arXiv preprint arXiv:1903.11024.
- Bian, J., Gao, B., & Liu, T. Y. (2014). Knowledge-powered deep learning for word embedding. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 132-148.
- Borko, H., & Bernick, M. (1963). Automatic document classification. Journal of the ACM (JACM), 10(2), 151-162.
- Cai, S., Palazoglu, A., Zhang, L., & Hu, J. (2019). Process alarm prediction using deep learning and word embedding methods. ISA Transactions, 85, 274-283.
- Habibi, M., Weber, L., Neves, M., Wiegandt, D. L., & Leser, U. (2017). Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics, 33(14), i37-i48.
- Jason Brownlee, (2017) How to Use Word Embedding Layers for Deep Learning with Keras. Eişim Adresi: http://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras.
- Jurafsky, Daniel; H. James, Martin (2000). Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, N.J.: Prentice Hall. ISBN 978-0-13-095069-7.
- Keras, (2021), Sequential_model. Erişim adresi: http://keras.io/guides/sequential_model.
- Kilimci, Z. H., & Akyokus, S. (2018). Deep learning-and word embedding-based heterogeneous classifier ensembles for text classification. Complexity.
- Kocmi, T., & Bojar, O. (2017). An exploration of word embedding initialization in deep-learning tasks. arXiv preprint arXiv:1711.09160.