AUTOMATED HELP DESK SYSTEM BASED ON DEEP LEARNING

Year 2022, Volume: 30 Issue: 3, 318 - 327, 21.12.2022

Metin Yılmaz , Efnan Şora Günal

https://doi.org/10.31796/ogummf.1038486

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

DERİN ÖĞRENME TEMELLİ OTOMATİK YARDIM MASASI SİSTEMİ

Abstract

Yardım masası, bir organizasyonun çalışanlarına veya müşterilerine merkezi bilgi ve destek yönetimi hizmeti sağlayan iletişim noktasıdır. Organizasyonun verimliliği açısından, yardım masasına gelen taleplerin doğru kategorilere ayrılarak, doğru kişilere ve zamanında yönlendirilmesi büyük önem arz etmektedir. Bu sebeple, bu çalışma kapsamında, derin öğrenmeye dayalı otomatik bir yardım sistemi önerilmiştir. Önerilen sistem, talepleri, başlıklarında yer alan cümlelere göre otomatik olarak uygun kategorilere ayırmaktadır. Bu işlem için kelime gömme (ing. word embedding) yöntemi kullanılmıştır. Metin ön işleme adımlarından sonra, üç katmanda (embedding, flatten ve dense) öğrenme gerçekleştirilerek, yardım masası taleplerinin ait olduğu kategori belirlenmektedir. Bu amaçla, kurumsal bir şirkete ait BT yardım masası talepleri kullanılmıştır. Dokuz farklı kategoride toplam 28.104 talepten oluşan veri kümesi, %60 eğitim, %20 doğrulama ve %20 test kümesine ayrılmıştır. Yapılan deneyler sonucunda %98’e ulaşan sınıflandırma doğruluğu, önerilen modelin otomatik bir yardım masası sistemi için iyi bir aday olduğunu ortaya koymuştur.

Keywords

Derin Öğrenme, Doğal Dil İşleme, Metin Sınıflandırma, Yardım Masası, Deep Learning Natural Language Processing Text Classification Help Desk, Deep Learning, Natural Language Processing, Text Classification, Help Desk

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